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Traditional multifucus image fusion often requires the inclusion of edge features, blurred details, and noise pollution when perturbed by noise. To address these problems, this study proposes a method for fusing noisy multifucus images using adaptive sparse and low-rank representations. The proposed method first decomposes the image into high- and low-frequency subband coefficients using a non-subsampled shearlet transform. Subsequently, the high-frequency energy components are fused and denoised using a low-rank representation. The corresponding fusion rules are then set using an adaptive sparse representation to fuse the low-frequency subband coefficients. The final fusion result is obtained by reconstructing the fused high- and low-frequency subband coefficients. Experimental results show that the proposed method outperforms traditional methods in terms of both subjective performance and objective indicators, making it a compelling fusion method for noisy multifucus images. [ABSTRACT FROM AUTHOR]

The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the loss of detail information in infrared and visible light image fusion, this paper proposes a novel image fusion method based on an improved adaptive dual-channel PCNN model in the non-subsampled shearlet transform (NSST) domain. Firstly, NSST is used to decompose the infrared and visible light images into a series of high-pass sub-bands and a low-pass sub-band, respectively. Next, the PCNN models are stimulated using the weighted sum of the eight-neighborhood Laplacian of the high-pass sub-bands and the energy activity of the low-pass sub-band. The high-pass sub-bands are fused using local structural information as the basis for the linking strength for the PCNN, while the low-pass sub-band is fused using a linking strength based on multiscale morphological gradients. Finally, the fused high-pass and low-pass sub-bands are reconstructed to obtain the fused image. Comparative experiments demonstrate that, subjectively, this method effectively enhances the contrast of scenes and targets while preserving the detail information of the source images. Compared to the best mean values of the objective evaluation metrics of the compared methods, the proposed method shows improvements of 2.35%, 3.49%, and 11.60% in information entropy, mutual information, and standard deviation, respectively. [ABSTRACT FROM AUTHOR]

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances. [ABSTRACT FROM AUTHOR]

X-ray imaging is a valuable non-destructive tool for examining bronze wares, but the complexity of the coverings of bronze wares and the limitations of single-energy imaging techniques often obscure critical details, such as lesions and ornamentation. Therefore, multiple imaging is required to fully present the key information of bronze artifacts, which affects the complete presentation of information and increases the difficulty of analysis and interpretation. Using high-performance image fusion technology to fuse X-ray images of different energies into one image can effectively solve this problem. However, there is currently no specialized method for the fusion of images of bronze artifacts. Considering the special requirements for the restoration of bronze artifacts and the existing fusion framework, this paper proposes a new method. It is a novel multi-scale morphological gradient and local topology-coupled neural P systems approach within the Non-Subsampled Shearlet Transform domain. It addresses the absence of a specialized method for image fusion of bronze artifacts. The method proposed in this paper is compared with eight high-performance fusion methods and validated using a total of six evaluation metrics. The results demonstrate the significant theoretical and practical potential of this method for advancing the analysis and preservation of cultural heritage artifacts. [ABSTRACT FROM AUTHOR]

X-ray imaging is a valuable non-destructive tool for examining bronze wares, but the complexity of the coverings of bronze wares and the limitations of single-energy imaging techniques often obscure critical details, such as lesions and ornamentation. Therefore, multiple imaging is required to fully present the key information of bronze artifacts, which affects the complete presentation of information and increases the difficulty of analysis and interpretation. Using high-performance image fusion technology to fuse X-ray images of different energies into one image can effectively solve this problem. However, there is currently no specialized method for the fusion of images of bronze artifacts. Considering the special requirements for the restoration of bronze artifacts and the existing fusion framework, this paper proposes a new method. It is a novel multi-scale morphological gradient and local topology-coupled neural P systems approach within the Non-Subsampled Shearlet Transform domain. It addresses the absence of a specialized method for image fusion of bronze artifacts. The method proposed in this paper is compared with eight high-performance fusion methods and validated using a total of six evaluation metrics. The results demonstrate the significant theoretical and practical potential of this method for advancing the analysis and preservation of cultural heritage artifacts.

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods. Based on traditional image processing techniques, this paper proposes a new hybrid image denoising model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain the first denoised image. The weighted kernel norm minimization (WNNM) and non-subsampled shearlet transform (NSST) algorithms are successively adopted to get the second denoised image. By the gradient domain guided filtering, the texture information of the first denoised image is extracted to enhance the details of the second denoised image. Specially, we propose the adaptive iterative NSST algorithm based on the improved soft thresholding, in order to solve the problems about the discontinuity of the hard thresholding and the constant deviation of the soft thresholding. Our approach can not only attenuate excessive smoothing, but also restore the natural appearance of the image. Experiments are conducted to demonstrate that our proposed method enjoys PSNR and SSIM performance gains over several deep learning denoising methods.

In order to solve the problems about color distortion and low contrast of underwater images, we propose an underwater image enhancement algorithm that combines adaptive color correction with improved Retinex algorithm. Our algorithm is a single-image enhancement method that does not require specialized hardware and underwater scenes prior. Firstly, the adaptive color correction is carried out on the underwater distorted images to solve the color cast problem effectively. Then, on the one hand, we use the image decomposition to strengthen the detail part and obtain a detail enhanced image. On the other hand, we use the improved Retinex algorithm to strengthen the edge part and obtain an edge enhanced image. Finally, the detail enhanced image and the edge enhanced image are fused based on the non-subsampled shearlet transform (NSST) to obtain the final enhanced underwater image. The results show that our method outperforms several state-of-the-art methods about underwater image enhancement in terms of PCQI, UCIQE, UIQM and IE. By scale invariant feature transform (SIFT) algorithm, we calculate the number of feature matching points of the input image and the enhanced image, and our proposed method achieves the best experimental results. The source code of our proposed algorithm is available at: https://github.com/lin9393/ underwater-image-enhance.

Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results. [ABSTRACT FROM AUTHOR]

To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency. [ABSTRACT FROM AUTHOR]

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image. [ABSTRACT FROM AUTHOR]

Remote sensing image fusion can effectively solve the inherent contradiction between spatial resolution and spectral resolution of imaging systems. At present, the fusion methods of remote sensing images based on multi-scale transform usually set fusion rules according to local feature information and pulse-coupled neural network (PCNN), but there are problems such as single local feature, as fusion rule cannot effectively extract feature information, PCNN parameter setting is complex, and spatial correlation is poor. To this end, a fusion method of remote sensing images that combines low-level visual features and a parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN) in a non-subsampled shearlet transform (NSST) domain is proposed in this paper. In the low-frequency sub-band fusion process, a low-level visual feature fusion rule is constructed by combining three local features, local phase congruency, local abrupt measure, and local energy information to enhance the extraction ability of feature information. In the process of high-frequency sub-band fusion, the structure and parameters of the dual-channel pulse-coupled neural network (DCPCNN) are optimized, including: (1) the multi-scale morphological gradient is used as an external stimulus to enhance the spatial correlation of DCPCNN; and (2) implement parameter-adaptive representation according to the difference box-counting, the Otsu threshold, and the image intensity to solve the complexity of parameter setting. Five sets of remote sensing image data of different satellite platforms and ground objects are selected for experiments. The proposed method is compared with 16 other methods and evaluated from qualitative and quantitative aspects. The experimental results show that, compared with the average value of the sub-optimal method in the five sets of data, the proposed method is optimized by 0.006, 0.009, 0.009, 0.035, 0.037, 0.042, and 0.020, respectively, in the seven evaluation indexes of information entropy, mutual information, average gradient, spatial frequency, spectral distortion, ERGAS, and visual information fidelity, indicating that the proposed method has the best fusion effect. [ABSTRACT FROM AUTHOR]

The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST's sub-bands, energy features are extracted and stored in the feature database for training. SVM classifier is used for the classification of skin lesion images. The dermoscopic skin images are obtained from PH² database which comprises of 200 dermoscopic color images with melanocytic lesions. The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic (ROC) curves. The SLC system achieves 96% classification accuracy using NSST's energy features obtained from 3rd level with 8-directions. [ABSTRACT FROM AUTHOR]

Medical images became a very important information tool for health professionals. Currently, the medical image acquired in a hospital or an imaging center can be shared among several health professionals to facilitate patient management and improve medical information management. In this work, we proposed a robust and blind watermarking approach to adequately secure medical images exchanged in telemedicine. This approach ensures the traceability and integrity of the medical and essential image for data security in the field of telemedicine. In this approach, the watermark consists of the patient's photography as well as the patient's data and the data related to the medical image acquisition. These combined data will thus guarantee the successful authentication of the image as well as the patient. A hash of this necessary information will be appropriately included in the watermark to ensure the integrity of the hidden data. The proposed watermarking in this approach remains a substitutive process. The frequency content of the image is acquired using transforms. Schur decomposition is then applied to the obtained mid-frequency subbands. Finally, the watermark bits will be substituted to the upper triangular matrix values obtained. Imperceptibility and robustness experimental results show that the proposed methods adequately maintain a significant quality of watermarked images and are remarkably robust against several conventional attacks. Since those schemes offer a reasonable imperceptibility and robustness, they could be useful for copyrights protection of medical images. [ABSTRACT FROM AUTHOR]

Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial–temporal characteristics, along with the dependence between variables. To address this limitation, we propose a novel method for multivariate time series forecasting based on nonlinear spiking neural P (NSNP) systems and non-subsampled shearlet transform (NSST). A multivariate time series is first converted into the NSST domain, and then NSNP systems are automatically constructed, trained, and predicted in the NSST domain. Because NSNP systems are used as nonlinear prediction models and work in the NSST domain, the proposed prediction method is essentially a multiscale transform (MST)–based prediction method. Therefore, the proposed prediction method can process nonlinear and non-stationary time series, and the dependence between variables can be characterized by the multiresolution features of the NSST transform. Five real-life multivariate time series were used to compare the proposed prediction method with five state-of-the-art and 28 baseline prediction methods. The comparison results demonstrate the effectiveness of the proposed method for multivariate time-series forecasting. • We propose nonlinear spiking neural P systems with global weights. • We propose a new NSNP-based prediction method in NSST for multivariate time series. [ABSTRACT FROM AUTHOR]

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms. [ABSTRACT FROM AUTHOR]

Image fusion has become a hot issue in the field of information processing. In this paper, a novel image fusion method based on guided filter random walk and improved spiking cortical model (ISCM) in non-subsampled shearlet transform (NSST) is presented. The core process is composed of three steps. Firstly, the source images to be fused are decomposed into the low-frequency parts and high-frequency parts via NSST. Then, two models including guided filter and random walk are combined to complete the fusion of low-frequency sub-images. As for the fusion of the high-frequency parts, the traditional spiking cortical model is improved to be ISCM to capture and fuse the details information of the source images. Finally, the fused image can be obtained by inverse NSST. In order to verify the effectiveness of the proposed method, lots of datasets with different categories are selected as the source images to conduct the simulation experiments. Experimental results demonstrate that the proposed method own obvious superiorities over the representative ones in terms of both subjective visual performance and objective evaluation data. [ABSTRACT FROM AUTHOR]

To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency.

In order to solve the problem of limited retention of detail information in the multi-focus image fusion algorithm, a multi-focus image fusion algorithm with improved sparse representation and integrated energy sum is proposed. Firstly, the non-subsampled shearlet transform is used on the source image to obtain low-frequency and high-frequency coefficient matrix. Secondly? the image block is extracted from the low-frequency coefficient matrix through the sliding window technique, a joint local a- daptive dictionary is constructed, and the sparse representation coefficients are calculated using the orthogonal matching tracking algorithm. Then, the sparse after fusion is obtained using the variance energy weighting rule coefficients? and the fused low-frequency coefficient matrix is obtained through the reverse sliding window technique. Thirdly, for the high-frequency coefficients, the integration rule of the integrated energy sum is proposed to obtain the fused high-frequency coefficient matrix. Finally? the fusion image is obtained by inverse transformation. The experimental results show that the algorithm can retain more detailed information and has certain advantages in visual quality and objective evaluation. [ABSTRACT FROM AUTHOR]

In remote sensing, Pansharpening process has great significance in many practical applications like map updating, hazard monitoring, target recognition and object classification. Satellite sensors capturing panchromatic and multispectral images with complementary characteristics due to tradeoff between IFOV (instantaneous field of view) and SNR (signal-to-noise ratio). Pansharpening is a process of combining PAN (panchromatic) image of high spatial resolution with MS (multispectral) image of high spectral resolution to get image of high spectral and spatial resolution. In Pansharpening, balancing between extraction of information and injection of information is crucial point; misbalancing can cause intensity distortion. Proposed method is a combination of CSC (convolution sparse coding) and adaptive PCNN (pulse coupled neural network) approach. NSST (non-sub-sampled shearlet transform) is used for band separation of PAN and MS image. CSC is used for fusing low pass sub-bands, and adaptive PCNN method is employed for fusing high pass sub-bands. Five datasets with different geographical areas like mountain, urban and vegetation area are used for experiment purpose. Visual results and quantitative index analysis reflect the superiority of proposed method in preserving spectral details in pansharpened image. [ABSTRACT FROM AUTHOR]

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks. [ABSTRACT FROM AUTHOR]

To solve the problems of low image contrast and low feature representation in infrared and visible image fusion, an image fusion algorithm based on latent low-rank representation (LatLRR) and non-subsampled shearlet transform (NSST) methods is proposed. First, infrared and visible images are decomposed into base subbands, saliency subbands and sparse noise subbands by the LatLRR model. Then, the base subbands are decomposed into low-frequency and high-frequency coefficients by NSST, and a feature extraction algorithm based on VGGNet and a logical weighting algorithm based on filtering are proposed to merge the coefficients. An adaptive threshold algorithm based on the regional energy ratio is proposed to fuse the saliency subbands. Finally, the fused base subbands are reconstructed, the sparse noise subbands are discarded, and a fused image is obtained by combining the subband information after fusion. Experimental results show that for the fused image produced, the algorithm performs well in both subjective and objective evaluation.

Featured Application: Our method can be applied to AFAM imaging, which helps to analyze cell structure. Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods. [ABSTRACT FROM AUTHOR]

In order to solve the problem that defects of different scales have different terahertz imaging characteristics in fiber reinforced composites, the fusion processing method of two terahertz images with complementary defect information was studied. To reduce the Gibbs phenomenon, Non-subsampled Shearlet Transform (NSST) with the property of shift- invariance was used to decompose source images and get their low-frequency subband and high frequency subband coefficients. Regional variance was used as connection strength factor of the Pulse Coupled Neural Network (PCNN) in the low frequency coefficient fusion, which is more according with human visual characteristics. In the fusion of high frequency coefficients, the Regional Gradient Energy of Direction Information Measure (RGEDIM) was introduced to extract the edge, texture and other details of the image and integrated them into the final image, the impact of noise on image fusion was reduced better. Finally, the fusion image was obtained through NSST inverse transform. The experimental results show that this method is superior to wavelet, Non-subsampled Contourlet Transform (NSCT) and traditional PCNN method, the fusion image has more mutual information and contains more original image information, all the defects of the source image can be clearly seen on the fusion image. [ABSTRACT FROM AUTHOR]

In this work, multiscale decomposition and sparse representation-based multimodal medical image fusion technique is proposed. An efficient denoising technique, feature-preserving regularized Savitzky–Golay filter is applied to obtain noise-free images. The filtered medical images are split into low- and high-pass subbands by non-subsampled shearlet transform (NSST). The sparse coefficient vectors of low-pass subbands are obtained from a pre-learned dictionary, and "max-L1" rule is applied to obtain the fused low-pass subband. However, high-pass subbands are fused using "max-absolute" rule. Lastly, NSST reconstruction is applied to generate the fused multimodal medical image. The non-subsampled contourlet transform, NSST-based fusion using parameter adaptive pulse coupled neural network and phase congruency techniques are also realized for comparative analysis. Multiple experiments on clean and noisy sets are performed for gray and color medical images. The fusion techniques are also tested on infrared–visible image pairs. The visual and quantitative outcomes verify that suggested technique outperforms the state-of-the-art fusion techniques. [ABSTRACT FROM AUTHOR]

Hyper-wavelet transforms, such as a non-subsampled shearlet transform (NSST), are one of the mainstream algorithms for removing random noise from ground-penetrating radar (GPR) images. Because GPR image noise is non-uniform, the use of a single fixed threshold for noisy coefficients in each sub-band of hyper-wavelet denoising algorithms is not appropriate. To overcome this problem, a novel NSST-based GPR image denoising grey wolf optimisation (GWO) algorithm is proposed. First, a time-varying threshold function based on the trend of noise changes in GPR images is proposed. Second, an edge area recognition and protection method based on the Canny algorithm is proposed. Finally, GWO is employed to select appropriate parameters for the time-varying threshold function and edge area protection method. The Natural Image Quality Evaluator is utilised as the optimisation index. The experiment results demonstrate that the proposed method provides excellent noise removal performance while protecting edge signals.

In order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.

The rolling guidance filtering (RGF) has a good characteristic which can smooth texture and preserve the edges, and non-subsampled shearlet transform (NSST) has the features of translation invariance and direction selection based on which a new infrared and visible image fusion method is proposed. Firstly, the rolling guidance filter is used to decompose infrared and visible images into the base and detail layers. Then, the NSST is utilized on the base layer to get the high-frequency coefficients and low-frequency coefficients. The fusion of low-frequency coefficients uses visual saliency map as a fusion rule, and the coefficients of the high-frequency subbands use gradient domain guided filtering (GDGF) and improved Laplacian sum to fuse coefficients. Finally, the fusion of the detail layers combines phase congruency and gradient domain guided filtering as the fusion rule. As a result, the proposed method can not only extract the infrared targets, but also fully preserves the background information of the visible images. Experimental results indicate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments. [ABSTRACT FROM AUTHOR]

Aiming at some problems in existing infrared and visible image fusion methods such as edge blurring, low contrast, loss of details, a novel fusion scheme based on non-subsampled shearlet transform (NSST), visual saliency and multi-objective artificial bee colony (MOABC) optimizing spiking cortical mode (SCM) is proposed. NSST has many advantages such as multi-scale features and sparse representation. Moreover, the visual saliency map can improve the low frequency fusion strategy, and SCM has coupling and pulse synchronization properties. Firstly, NSST is utilized to decompose the source image into a low-frequency subband and a series of high-frequency subbands. Secondly, the low-frequency subband is fused by SCM, where SCM is motivated by the edge saliency map of the low-frequency subband of the source image, and then the high-frequency subbands are also fused by SCM, where the modified spatial frequency of the high-frequency subbands of the source image is adopted as the input stimulus of SCM, the parameters of SCM are optimized by the novel multi-objective artificial bee colony technique. Finally, the fused image is reconstructed by inverse NSST. Experimental results indicate that the proposed scheme performs well and has obvious superiorities over other current typical ones in both subjective visual performance and objective criteria. [ABSTRACT FROM AUTHOR]

Highlights • This paper makes use of a deep CNN for image denoising. • The network is trained using edges given by the canny algorithm. • The proposed method can better recover edges of noisy images. Abstract This paper introduces a novel denoising approach making use of a deep convolutional neural network to preserve image edges. The network is trained by using the edge map obtained from the well-known Canny algorithm and aims at determining if a noisy patch in non-subsampled shearlet domain corresponds to the location of an edge. In the first step of the proposed denoising algorithm, we use the non-subsampled shearlet transform to decompose the noisy image into a low-frequency subband and a series of high-frequency subbands. Subsequently, 3D blocks are formed by stacking 2D blocks of high-frequency subbands along a specific direction. Each 3D patch is then fed to the trained deep convolutional neural network to determine if it belongs to the edge-related class or not. Finally, the NSST (non-subsampled shearlet transform) coefficients belonging to the edge-related class remain unchanged, and those not belonging to the edge-related class are denoised by the shrinkage method using an adaptive threshold. Experimental results on various test images including benchmark grayscale images and medical ultrasound images demonstrate that the proposed method achieves better performance compared to some state-of-the-art denoising approaches. [ABSTRACT FROM AUTHOR]

The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images based on convolutional neural networks (CNNs) and a dual-channel spiking cortical model (DCSCM). Firstly, non-subsampled shearlet transform (NSST) is utilized to decompose the source image into a low-frequency coefficient and a series of high-frequency coefficients. Secondly, the low-frequency coefficient is fused by the CNN framework, where weight map is generated by a series of feature maps and an adaptive selection rule, and then the high-frequency coefficients are fused by DCSCM, where the modified average gradient of the high-frequency coefficients is adopted as the input stimulus of DCSCM. Finally, the fused image is reconstructed by inverse NSST. Experimental results indicate that the proposed scheme performs well in both subjective visual performance and objective evaluation and has superiorities in detail retention and visual effect over other current typical ones. Graphical abstract A schematic diagram of the CT and MRI medical image fusion framework using convolutional neural network and a dual-channel spiking cortical model. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • Shearlet-based automatic de-noising method. • Method intelligently adapts to type of noise. • Supports use of HSI for process analysis. • Can deal with noise present in consecutive wavelengths. Abstract Hyperspectral imaging (HSI) has become an essential tool for exploration of different spatially-resolved properties of materials in analytical chemistry. However, due to various technical factors such as detector sensitivity, choice of light source and experimental conditions, the recorded data contain noise. The presence of noise in the data limits the potential of different data processing tasks such as classification and can even make them ineffective. Therefore, reduction/removal of noise from the data is a useful step to improve the data modelling. In the present work, the potential of a wavelength-specific shearlet-based image noise reduction method was utilised for automatic de-noising of close-range HS images. The shearlet transform is a special type of composite wavelet transform that utilises the shearing properties of the images. The method first utilises the spectral correlation between wavelengths to distinguish between levels of noise present in different image planes of the data cube. Based on the level of noise present, the method adapts the use of the 2-D non-subsampled shearlet transform (NSST) coefficients obtained from each image plane to perform the spatial and spectral de-noising. Furthermore, the method was compared with two commonly used pixel-based spectral de-noising techniques, Savitzky-Golay (SAVGOL) smoothing and median filtering. The methods were compared using simulated data, with Gaussian and Gaussian and spike noise added, and real HSI data. As an application, the methods were tested to determine the efficacy of a visible-near infrared (VNIR) HSI camera to perform non-destructive automatic classification of six commercial tea products. De-noising with the shearlet-based method resulted in a visual improvement in the quality of the noisy image planes and the spectra of simulated and real HSI. The spectral correlation was highest with the shearlet-based method. The peak signal-to-noise ratio (PSNR) obtained using the shearlet-based method was higher than that for SAVGOL smoothing and median filtering. There was a clear improvement in the classification accuracy of the SVM models for both the simulated and real HSI data that had been de-noised using the shearlet-based method. The method presented is a promising technique for automatic de-noising of close-range HS images, especially when the amount of noise present is high and in consecutive wavelengths. [ABSTRACT FROM AUTHOR]

Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent processing which is also important for the denoising. In this paper, we propose a novel blind universal image denoiser to remove synthesis and realistic noise while preserving the image texture. The denoiser consists of noise network and prior network parallelly, and then a fusion block is used to give the weight between these two networks to balance computation cost and denoising performance. We also use the Non-subsampled Shearlet Transform (NSST) to enlarge the size of receptive field to obtain more detailed information. Extensive denoising experiments on synthetic images and realistic images show the effectiveness of our denoiser. [ABSTRACT FROM AUTHOR]

Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods.

The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.

Content-based image retrieval (CBIR) has been an active research topic in the last decade. Multiple feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a new CBIR method based on an efficient integration of texture and shape features. The texture features are extracted on the decomposed images processed by the optimal non-subsampled shearlet transform (NSST), and are represented by the high-frequency sub-band coefficients, which can be modeled by Bessel K Form (BKF) distribution; the shape features are represented by low-order quaternion polar harmonic transforms (QPHTs). The two kinds of features are then integrated by a weighted distance measurement, where Kullback-Leibler distance (KLD) and Euclidean distance (ED) are used for texture and shape features respectively. The integration of shape and texture information provides a robust feature set for image retrieval. Experimental results on standard benchmarks show significant improvements on retrieval performance using the proposed method compared with previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • This work proposes a new scheme to fuse CT, MRI and PET images into a single image. • A two-stage method is designed in HSV color space to fuse the three kinds of images. • Two simplified PCNN models are used to fuse the high frequency coefficients of NSST. • Results show the performance of the proposed scheme is better than conventional methods. Abstract Computational imaging plays an important role in medical treatment for providing more comprehensive medical images. This work proposes a new scheme to fuse computed tomography (CT), magnetic resonance (MRI), and positron emission tomography (PET) images into a single image. A novel two-stage medical image fusion scheme, which is based on non-subsampled shearlet transform (NSST) and simplified pulse coupled neural networks (S-PCNNs), is proposed in the hue-saturation-value (HSV) color space. Firstly, CT and MRI images are decomposed into a set of low and high frequency coefficients by NSST, PET images are transformed into the HSV color space, and then the V component of PET image in the HSV color space. Secondly, intersecting cortical models (ICMs) are utilized to extract the edges and outlines in a larger area from the high frequency coefficients, and S-PCNNs are employed to describe the subtly detailed information in a smaller area. Thirdly, different fusion rules are designed to fuse the corresponding low and high frequency coefficients. At last, the fused medical image is obtained by the inverse HSV and NSST transformation, successively. The experimental results show that the proposed scheme is effective, and it can fuse more information into the final images than conventional methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose an aerial image matching algorithm based on NSST and quaternion exponential moment. Firstly, we use nonsubsampled shearlet transform (NSST) to decompose the reference image and the to-be-matched image, and the scale invariant feature with error resilience (SIFER) operator is used to extract stable feature points from NSST low-frequency sub-bands and construct local feature areas respectively. Subsequently, local features of each feature area are solved by quaternion exponential moment to constitute feature vectors of such feature points for pre-matching. In the end, mismatching point pairs are removed by the random sample consensus (RANSAC) algorithm. Finally, experimental results show that compared with the SIFT and SURF algorithms, the algorithm proposed in this paper makes faster operations, has higher matching precision, and is significantly better than the other two methods in resisting rotation, noise, brightness change, and integrated disturbance. [ABSTRACT FROM AUTHOR]