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The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the "superimage" and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obtain the "superimage". The DSMT-NLM incorporates the concept of directional segmentation and extends the application of the non-local means (NLM) algorithm to multi-temporal images. Simultaneously, the enhanced framework employs a weighted averaging method based on wavelet transform (WAMWT) to generate superimposed images, thereby enhancing the generation process of ratio images. Experimental results demonstrate that compared to RABASAR, Frost, and NLM, the proposed method exhibits outstanding performance. It not only effectively removes speckle noise from multi-temporal SAR images and reduces the generation of false details, but also successfully achieves the fusion of multi-temporal information, aligning with experimental expectations. [ABSTRACT FROM AUTHOR]

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified. [ABSTRACT FROM AUTHOR]

The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the “superimage” and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obtain the “superimage”. The DSMT-NLM incorporates the concept of directional segmentation and extends the application of the non-local means (NLM) algorithm to multi-temporal images. Simultaneously, the enhanced framework employs a weighted averaging method based on wavelet transform (WAMWT) to generate superimposed images, thereby enhancing the generation process of ratio images. Experimental results demonstrate that compared to RABASAR, Frost, and NLM, the proposed method exhibits outstanding performance. It not only effectively removes speckle noise from multi-temporal SAR images and reduces the generation of false details, but also successfully achieves the fusion of multi-temporal information, aligning with experimental expectations.

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.

• A thresholding scheme based on a non-local average filter approach is used to segment cells. • The SqueezeNet architecture is used to reduce these problems and overcome the problems in blood cell classification. • The proposed method has improved the accuracy to 99.83%, 99.67%, 99.56% and 99.02% The major issue in classifying blood cells from the microscopic image is overlapping cells and overfitting data. Therefore, to solve these problems in microscopic imaging, a thresholding scheme based on a non-local average filter approach is used to segment cells that separate the overlap of cells and thus perform the segmentation procedure. Basic convolutional neural networks (CNN) such as AlexNet, ResNet, and DenseNet have proven to be more powerful in image classification in various fields. However, the problem is that the number of parameters used in the network increased, which tends to separate the optimization procedure; otherwise, it leads to complexity in processing (increased simulation time). The SqueezeNet architecture is used to reduce these problems and overcome the problems in blood cell classification, like the overfitting of data and gradient vanishing problems. That will extract and classify the features based on the ground truth with 5 times fewer parameters than the traditional CNN architectures. The proposed method has improved the accuracy to 99.83%, 99.67%, 99.56% and 99.02% for the test datasets of kaggle-blood cell image, LISC dataset, Raabin-WBC and BCCD dataset, respectively. [ABSTRACT FROM AUTHOR]

Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as well as Gaussian noise. Generally the effect of noise can be reduced using smooth filters for better results. Here, Laplacian of Gaussian (LoG) filter is applied for categorizing the edge and noisy pixels. Before that it is mandatory to obtain local smoothing of pixels. Finally the system performance is improved by averaging the non-local parameters. This is applicable to medical images also for removing impulse noise as well as Gaussian noise. The algorithm has been tested with MRI images and CT images efficiently. Better results are obtained in comparison with the previous methods with respect to better visual quality, PSNR and SSIM. [ABSTRACT FROM AUTHOR]

Abstract Background We investigate the use of different denoising filters on low signal-to-noise ratio cardiac images of the rat heart acquired with a birdcage volume coil at 7T. Accuracy and variability of cardiac function parameters were measured from manual segmentation of rat heart images with and without filtering. Methods Ten rats were studied using a 7T Varian system. End-diastolic and end-systolic volumes, ejection fraction and left ventricle mass (LVM) were calculated from manual segmentation by two experts on cine-FLASH short-axis slices covering the left ventricle. Series were denoised with an anisotropic diffusion filter, a whole variation regularization or an optimized Rician non-local means (ORNLM) filtering technique. The effect of the different filters was evaluated by the calculation of signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, followed by a study of intra- and inter-expert variability of the measurement of physiological parameters. The calculated LVM was compared to the LVM obtained by weighing the heart ex vivo. Results The SNR and the CNR increased after application of the different filters. The performance of the ORNLM filter was superior for all the parameters of the cardiac function, as judged from the inter- and intra-observer variabilities. Moreover, this filtering technique resulted in the lowest variability in the LVM evaluation. Conclusions In cardiac MRI of rats, filtering is an interesting alternative that yields better contrast between myocardium and surrounding tissues and the ORNLM filter provided the largest improvements.

Abstract Background Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.

• A newpost-processing technique for EBSD patternsis proposed. • The non-local smoothing uses larges kernels but preserves edges and sharp features. • By improving signal-to-noise ratios, indexing success rates are greatly improved. • Capabilities demonstrated through application to nickel and aluminum materials. Due to continued advances in phosphor sensitivity and camera technology, electron backscattered diffraction (EBSD) within a scanning electron microscope (SEM) has become an increasingly popular method for determining crystal orientations within a given microstructure. Concurrent advances in computational processing have also made it possible to store each individual diffraction pattern as it is collected, which has allowed more complex algorithms to be deployed for post-processing and indexing patterns. This paper proposes a new post-processing technique for pattern enhancement that aids in re-indexing by leveraging a non-local smoothing kernel whose weights are based on the exponential decay of the Euclidean distance between patterns. The advantage of this approach is its ability to utilize very large smoothing kernels without losing integrity near interface boundaries and while still operating on timescales comparable to traditional indexing approaches. Using an Inconel 600 nickel alloy sample, the capabilities and performance of the proposed approach are compared to other indexing schemes, including neighbor pattern averaging with re-indexing (NPAR) and a dictionary-based approach. The results demonstrate that the proposed method consistently produces a higher index success rate (ISR) than NPAR and comparable ISRs to the dictionary-based approach, a method with orders-of-magnitude greater computational demands. Source code for the NLPAR algorithm is available at https://github.com/USNavalResearchLaboratory/NLPAR [ABSTRACT FROM AUTHOR]

• A method of improving the signal-to-noise ratio of DAS system is studied. • A one-dimensional non-local mean filtering method for DAS system signal processing is proposed. • The coherent fading problem in DAS system is effectively suppressed. • Improves the location and identification accuracy of target events. Distributed optical fiber acoustic sensing (DAS) is a kind of optical fiber sensing system based on phase sensitive optical time domain reflection technology (Ф-OTDR). It has the characteristics of high sensitivity, long distance and real-time detection, and has been widely used in the positioning and monitoring of various engineering events. However, the high coherent detection light source used in the Ф-OTDR technology will produce coherent fading phenomenon in the fiber, which causes fading noise. This will seriously affecting the monitoring effect of the DAS system. Therefore, in order to address the fading noise problem in DAS system, a system fading noise suppression method based on non-local means filtering is proposed, which can effectively suppress the system fading noise in the signals and improve signal-to-noise ratio while preserving the characteristics of the target event signals. The experimental results show that the proposed method has better DAS system noise suppression effect in the face of different intensities and types of noise interference. Further, the proposed method is compared with EEMD, wavelet and outlier noise suppression methods. The results show that the noise suppression effect is significantly better than other methods, and the signal-to-noise ratio is improved by more than 30%, which verifies the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing. [ABSTRACT FROM AUTHOR]

Firstly, pseudo median filtering algorithm was improved. Factors of gray value and geometric distance of pixels are integrated into noise detection process so as to separate noise from image pixels. The noise is filtered by weighted filtering method. Then obtaining method of prior information of an improved non-local means filtering algorithm was improved. The noise image is conducted lifting wavelet transform. The low-frequency coefficients are selected by a new threshold function, and the selected low-frequency coefficients which are greater than the threshold are reconstructed to obtain a reference image. Weighted value of the reference image is computed and regarded as prior information. Finally, a filtering method for infrared image based on the two improved algorithms was proposed, namely using the two improved algorithms to filter noise in infrared image successively, then correcting the filtered image by the reference image. The experimental results show that the method has good filter effect for infrared image with high density noises.

Background: We investigate the use of different denoising filters on low signal-to-noise ratio cardiac images of the rat heart acquired with a birdcage volume coil at 7T. Accuracy and variability of cardiac function parameters were measured from manual segmentation of rat heart images with and without filtering. Methods: Ten rats were studied using a 7T Varian system. End-diastolic and end-systolic volumes, ejection fraction and left ventricle mass (LVM) were calculated from manual segmentation by two experts on cine-FLASH short-axis slices covering the left ventricle. Series were denoised with an anisotropic diffusion filter, a whole variation regularization or an optimized Rician non-local means (ORNLM) filtering technique. The effect of the different filters was evaluated by the calculation of signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, followed by a study of intra- and inter-expert variability of the measurement of physiological parameters. The calculated LVM was compared to the LVM obtained by weighing the heart ex vivo. Results: The SNR and the CNR increased after application of the different filters. The performance of the ORNLM filter was superior for all the parameters of the cardiac function, as judged from the inter- and intra-observer variabilities. Moreover, this filtering technique resulted in the lowest variability in the LVM evaluation. Conclusions: In cardiac MRI of rats, filtering is an interesting alternative that yields better contrast between myocardium and surrounding tissues and the ORNLM filter provided the largest improvements. [ABSTRACT FROM AUTHOR]

In multifocus image fusion, accurate detection of the focused pixel from source images is crucial to improve the quality of fused result. Traditionally, the method developed in spatial domain is commonly used. However, such approaches tend to produce boundary seams or distortions. To this end, we propose a new fixed window technique of multiscale image analysis (MIA) and a new weighted fusion strategy by employing non-local means filtering (NLF). This new scheme consists of three parts: detection of focused pixel, correction of detecting results, and generation of fusion weight maps for source images. To improve detection robustness against the size of object, we develop the fixed window technique of MIA to detect the focused pixels, and then we construct the initial fusion decision map for each of source images by combining those detection results; second, we present a new refining process based on block consistency evaluation for correcting the initial detection result. At last, the corresponding source images are used as a guide and combined with the NLF to produce the fusion weight maps. Experimental results demonstrate that the performance of our approach is superior to that of many state-of-the-art algorithms in terms of both visual perception and objective evaluation. [ABSTRACT FROM AUTHOR]

Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach. [ABSTRACT FROM AUTHOR]

Purpose To develop and evaluate an image-domain noise reduction method for multi-energy CT ( MECT) data. Methods Multi-Energy Non-Local Means ( MENLM) is a technique that uses the redundant information in MECT images to achieve noise reduction. In this method, spatio-spectral features are used to determine the similarity between pixels, making the similarity evaluation more robust to image noise. The performance of this MENLM filter was tested on images acquired on a whole-body research photon counting CT system. The impact of filtering on image quality was quantitatively evaluated in phantom studies in terms of image noise level (standard deviation of pixel values), noise power spectrum ( NPS), in-plane and cross-plane spatial resolution, CT number accuracy, material decomposition performance, and subjective low-contrast spatial resolution using the American College of Radiology ( ACR) CT accreditation phantom. Clinical feasibility was assessed by performing MENLM on contrast-enhanced swine images and unenhanced cadaver head images using clinically relevant doses and dose rates. Results The phantom studies demonstrated that the MENLM filter reduced noise substantially and still preserved the shape and peak frequency of the NPS. With 80% noise reduction, MENLM filtering caused no degradation of high-contrast spatial resolution, as illustrated by the modulation transfer function ( MTF) and slice sensitivity profile ( SSP). CT number accuracy was also maintained for all energy channels, demonstrating that energy resolution was not affected by filtering. Material decomposition performance was improved with MENLM filtering. The subjective evaluation using the ACR phantom demonstrated an improvement in low-contrast performance. MENLM achieved effective noise reduction in both contrast-enhanced swine images and unenhanced cadaver head images, resulting in improved detection of subtle vascular structures and the differentiation of white/gray matter. Conclusion In MECT, MENLM achieved around 80% noise reduction and greatly improved material decomposition performance and the detection of subtle anatomical/low-contrast features while maintaining spatial and energy resolution. MENLM filtering may improve diagnostic or functional analysis accuracy and facilitate radiation dose and contrast media reduction for MECT. [ABSTRACT FROM AUTHOR]

Purpose Limited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed. Methods Since the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV. Results Simulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm. Conclusion This paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of dimensional parameters from noisy atomic force microscopy (AFM) images was investigated. Non-local means (NLM) denoising was adopted to reduce noise and maintain fine image structures. Major tuning parameters in NLM filtering, such as the patch size and the window size, were optimized on simulated surface structures. The ability of dimensional evaluation from noisy data was demonstrated to be improved by almost 15 times. Finally, NLM filtering with optimal settings was applied on experimental AFM images, which were scanned on a patterned few-layer graphene specimen. Evaluations of the step height and the pattern size were verified to be much more accurate and robust. Such a data processing method can enhance the AFM dimensional measurements, particularly when the noise-level is reached. SCANNING 38:113-120, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography (TOF-MRA) images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field (MRF). Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode (FMM) to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization (PSO) algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a non-local, transform domain noise suppression framework to improve the quality of seismic reflection data. The original non-local means (NLM) algorithm measures similarities in the data domain and we generalize it in the nonsubsampled contourlet transform (NSCT) domain. NSCT gives a multiscale, multiresolution and anisotropy representation of the noisy input. The redundancy information in NSCT subbands can be utilized to enhance the structures in the original seismic data. Like the wavelet transform, NSCT coefficients in each subband follow the generalized Gaussian distribution and the parameters can be estimated using appropriate techniques. These parameters are used to construct our proposed NSCT domain filtering algorithm. Applications for synthetic and real seismic data of the proposed algorithm demonstrate its effectiveness on seismic data random noise suppression. [Copyright &y& Elsevier]

Abstract: Kernel regression is a non-parametric estimation technique which has been successfully applied to image denoising and enhancement in recent times. Magnetic resonance 3D image denoising has two features that distinguish it from other typical image denoising applications, namely the tridimensional structure of the images and the nature of the noise, which is Rician rather than Gaussian or impulsive. Here we propose a principled way to adapt the general kernel regression framework to this particular problem. Our noise removal system is rooted on a zeroth order 3D kernel regression, which computes a weighted average of the pixels over a regression window. We propose to obtain the weights from the similarities among small sized feature vectors associated to each pixel. In turn, these features come from a second order 3D kernel regression estimation of the original image values and gradient vectors. By considering directional information in the weight computation, this approach substantially enhances the performance of the filter. Moreover, Rician noise level is automatically estimated without any need of human intervention, i.e. our method is fully automated. Experimental results over synthetic and real images demonstrate that our proposal achieves good performance with respect to the other MRI denoising filters being compared. [Copyright &y& Elsevier]

Abstract: Surface mesh denoising is an important preprocess for many digital geometrical applications. Unlike most previous smoothing algorithms, which are vertex- or normal-based, we present a smoothing approach based on gradient field. The proposed algorithm first applies an extended non-local means image filter to diffuse mesh gradient field and then reconstructs a smoothed surface from the smoothed field by solving the Poisson equations in the least-squares sense. Experimental results on various data demonstrate that the proposed approach can successfully remove noise from meshes while preserving geometrical detail features of the original object and avoiding significant volume shrinkage. [ABSTRACT FROM AUTHOR]

• Temporal non-local means filtering improves SNR but preserves functional boundaries. • GPDF filtering with optimized kernel and parameter is crucial for optimal results. • Individual parcellation results are substantially improved after GPDF filtering. Characterizing functional brain connectivity using resting functional magnetic resonance imaging (fMRI) is challenging due to the relatively small Blood-Oxygen-Level Dependent contrast and low signal-to-noise ratio. Denoising using surface-based Laplace-Beltrami (LB) or volumetric Gaussian filtering tends to blur boundaries between different functional areas. To overcome this issue, a time-based Non-Local Means (tNLM) filtering method was previously developed to denoise fMRI data while preserving spatial structure. The kernel and parameters that define the tNLM filter need to be optimized for each application. Here we present a novel Global PDF-based tNLM filtering (GPDF) algorithm that uses a data-driven kernel function based on a Bayes factor to optimize filtering for spatial delineation of functional connectivity in resting fMRI data. We demonstrate its performance relative to Gaussian spatial filtering and the original tNLM filtering via simulations. We also compare the effects of GPDF filtering against LB filtering using individual in-vivo resting fMRI datasets. Our results show that LB filtering tends to blur signals across boundaries between adjacent functional regions. In contrast, GPDF filtering enables improved noise reduction without blurring adjacent functional regions. These results indicate that GPDF may be a useful preprocessing tool for analyses of brain connectivity and network topology in individual fMRI recordings. [ABSTRACT FROM AUTHOR]