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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.)

The priority during speckle reduction in Polarimetric Synthetic Aperture Radar (Pol-SAR) data is to maintain the polarization information. For this reason, it is undesirable to separate each element of polarimetric SAR. Based on the multiplicative noise model and complex matrix, an effective distance similarity measure method is derived, which can process covariance matrix C or coherent matrix T directly. Experiments show that this method can effectively reduce speckle and maintain the polarization information, point targets, and texture structure. Moreover, it can handle large polarimetric SAR with simplicity and ease.

In this paper, an image denoising feedback framework is proposed for both color and range images. The proposed method works on an error minimization principle using split Bregman method. At first image is denoised by computing means in the local neighborhood. The pixels that have big differences from the center of the local neighborhood compared to the noise variance are then extracted from the denoised image. There is a low correlation between the extracted pixels and their local neighborhood. This information is fed to the feedback function and denoising is performed again, iteratively, to minimize the error. In most cases, the proposed framework yields best results both qualitatively and quantitatively. It shows better denoising results than the bilateral filtering when the edge information in the input images is affected by intense noise. Moreover, during the denoising process feedback function ensures that the edges are not over smoothed. The proposed framework is applied to denoise both color and range images, which shows it works effectively on a wide variety of images unlike the evaluated state-of-the-art denoising methods. [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.)

In the traditional bilateral filtering algorithm, the spatial parameter and gray parameter need to be predefined. The parameters are fixed and lack of adaptability. In order to solve this problem,this paper proposed a novel adaptive bilateral filtering algorithm based on parameter estimation. The algorithm obtained the geometric parameter according to the gray level co-occurrence matrix and got the photometric parameter via estimating noise standard deviation of smooth domain. Experimental re-sults show the effectiveness of the modified adaptive bilateral filtering in both subjective effects and objective image quality assessment. [ABSTRACT FROM AUTHOR]

High resolution remote sensing image segmentation methods that consider only the spectral information in the region growing process often lead to over segmentation and low boundary precision. To overcome that, a watershed transform algorithm which combines spectral information and texture information is proposed. At first, the spectral intensity gradient and the texture gradient have to be extracted from the input image. For that purpose, a new bilateral filtering model is introduced. This edge preserving algorithm can remove noise of images. Meanwhile, it can also remove texture from images by using a local smoothing scale parameter. By adapting this filtering algorithm on the original image and the Gabor texture feature images, the spectral information and texture information are extracted separately. Then with edge detection algorithm, the spectral intensity gradient and texture gradient are obtained. Finally a gradient fusion strategy by morphological dilation and watershed transform are performed in succession. Experiments are carried out on three high resolution color remote sensing images. Compared with JSEG and multi-resolution segmentation methods, the proposed method has a higher boundary precision and can reduce the over segmentation and under segmentation effects. [ABSTRACT FROM AUTHOR]

The bilateral filter is a fundamental smoothing tool in image processing and computer vision due to its outstanding edge‐preserving ability. However, the computational complexity depends on the size of the support of the spatial kernel. This drawback makes bilateral filtering time‐consuming and significantly limits its applications. A novel strategy to turn the range kernel of the bilateral filter into a sum of exponential functions is proposed. As the convolution with the exponential function can be accelerated by the O(1) box filtering, the computational complexity of the bilateral thus becomes O(1). Experimental results disclose that the proposed algorithm is competitive with the state‐of‐the‐art algorithms in terms of filtering accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

• A new laser ultrasonic imaging approach, in conjunction with the entropypolarized bilateral filtering and minimum variance beamforming algorithm; • Proven capability to precise image a submillimeter defect in a thick waveguide with a thickness ∼25 times the wavelength of the laser-induced shear wave; • Capability to process laser ultrasonic signals with SNR lower than 1 dB, benefiting practical laser ultrasonic applications; • A fully contactless solution to implementing laser ultrasonic imaging; and. • Validation using experiment. Recent quantum leap in far-field laser techniques has advanced noncontact implementation of nondestructive ultrasonic imaging, in pursuit of enhanced accessibility, detectability and practicability. Nevertheless, when laser-generated thermoelastic waves are extended to thick waveguides, they manifest fairly low signal-to-noise ratios (SNRs), along with severe wave diffusion, consequently lowering image resolution and contrast. With these motivations, a laser-ultrasonics imaging approach is developed, in conjunction with i) entropy-polarized bilateral filtering (Entropy-P-BF) for signal denoising, and ii) minimum variance (MV) beamforming for defect imaging, targeting at precise characterization of a submillimeter defect (with its characteristic dimension being smaller than the wave diffraction limit) in a thick waveguide. The entropy-polarized bilateral filtering denoises laser-induced ultrasonic wave signals via a two-dimensional convolution, the weight matrices of which are continuously updated according to local noise and uncertainty. With an elevated SNR, MV beamforming subsequently conducts an apodized beamforming to image the defect. Experimental validation is conducted by imaging a void-type defect, 0.7 mm only in its diameter, in a jet aero-engine turbine disk. Results prove that the developed approach is capable of characterizing a submillimeter defect accurately in a thick waveguide with thickness ∼25 times the wavelength of laser-induced shear wave, regardless of a fairly low SNR (<1dB). [ABSTRACT FROM AUTHOR]

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.)

This study introduces Soft Bilateral Filtering Shadows method of dynamic scenes, which uses multi-matrices of the light sample points due to lack realism in soft shadows generation in real time. While geometry-based shadow algorithm requires one pass per polygon for rendering shadow that requires time-consuming, the adopted shadow map algorithm needs a single rendering pass for each sample point of the light source to generate shadow at low cost. This method renders a complex scenes and accurately eliminating the inherent deficiencies in shadow maps. In order to compute shadow maps, view matrices were used for each sample point of the extended light source. Then penumbra region was used for interpolation based on bilateral filtering to create the soft shadows. They depend on multiple shadow maps which provide antialiasing shadow maps. The method uses fragment shader for rendering multiple shadow maps with penumbra and umbra regions. The main contribution of this article is introducing interpolation bilaterally of image-based shadows. This method makes the most effect of the computation significantly appear at the edges of the penumbra region. Furthermore, the filtering allows to obtain on the soft shadow marvelously at the lowest number possible of the light sample points. The generated soft shadows have good performance and high quality therefore, they are suitable for interactive applications. [ABSTRACT FROM AUTHOR]

Background and objective This paper introduces an effective noise removal method for medical ultrasound volume data. Ultrasound data usually need to be filtered because they contain significant noise. Conventional two-dimensional (2D) filtering methods cannot use the implicit information between adjacent layers, and existing 3D filtering methods are slow because of complicated filter kernels. Even though one filter method utilizes simple filters for speed, it is inefficient at removing noise and does not take into account the characteristics of ultrasound sampling. To solve this problem, we introduce a fast filtering method using parallel bilateral filtering and adjust the filter window size proportionally according to its position. Methods We devised a parallel bilateral filtering by obtaining a 3D summed area table of a quantized spatial filter. The filtering method is made adaptive by changing the kernel window size according to the distance from the ultrasound signal transmission point. Results Experiments were performed to compare the noise removal and loss of original data of the anisotropic diffusion filtering, bilateral filtering, and adaptive bilateral filtering of ultrasound volume-rendered images. The results show that the adaptive filter correctly takes into account the sampling characteristics of the ultrasound volumes. Conclusions The proposed method can more efficiently remove noise and minimize distortion from ultrasound data than existing simple or non-adaptive filtering methods. [ABSTRACT FROM AUTHOR]

In order to improve FCM segmentation, this paper proposed a new image denoising method. The traditional FCM method was sensitive to SAR image speckle noise. The proposed method worked in three steps. First of all, this paper used the wavelet transform method combined with Lee filtering method to denoise two SAR images respectively. The two images were of homologous phase from same area. Then, the logarithmic ratio detection method achieved the change information. With the bi-lateral filter, the denoising result could keep ample details of image. Finally, the FCMmethod divided the change information into two classes, and achieved the change detection result. This method took the parts of image of Ottawa area as data set for the performance evaluation. Compared with other denoising methods, the results show that the classification accuracy of the proposed algorithm reaches 98.29%,so it is better than other algorithms. [ABSTRACT FROM AUTHOR]

Rubbing is one of the most universal and perhaps the oldest of the techniques that have been used in printmaking. A carefully made rubbing provides an accurate and full-scale facsimile of the surface reproduced. However, many rubbing have been destroyed or lacked a good ways to identify them by certain events, while some other contained a large white background, or have become illegible due to erosion. In order to correct interpretation of these images, some image restoration techniques are employed. Image denoising is one of the important fields in the restoration arena. But, a great challenge of image denoising is how to preserve the edges and all fine details of a rubbing image while reducing the noise. This paper presents a comprehensive comparative study of image denoising techniques relying on Anisotropic Diffusion filter, Wiener filter, TV (Total Variation), NLM (Non-Local Means, NLM), Bilateral filtering. A quantitative measure of comparison is provided by the PSNR, MSE, SNR, UQI and SSIM of the image. Finally, the paper also analyzes its effect of denoising on rubbings with various algorithm and points out the advantages and disadvantages in application. [ABSTRACT FROM AUTHOR]

The locality sensitive histogram (LSH) injects spatial information into the local histogram in an efficient manner, and has been demonstrated to be very effective for visual tracking. In this paper, we explore the application of this efficient histogram in two important problems. We first extend the LSH to linear time bilateral filtering, and then propose a new type of histogram for efficiently computing edge-preserving nearest neighbor fields (NNFs). While the existing histogram-based bilateral filtering methods are the state of the art for efficient grayscale image processing, they are limited to box spatial filter kernels only. In our first application, we address this limitation by expressing the bilateral filter as a simple ratio of linear functions of the LSH, which is able to extend the box spatial kernel to an exponential kernel. The computational complexity of the proposed bilateral filter is linear in the number of image pixels. In our second application, we derive a new bilateral weighted histogram (BWH) for NNF. The new histogram maintains the efficiency of LSH, which allows approximate NNF to be computed independent of patch size. In addition, BWH takes both spatial and color information into account, and thus provides higher accuracy for histogram-based matching, especially around color edges. [ABSTRACT FROM PUBLISHER]

This paper presents a comparison between different techniques of noise removal such as NL-means algorithm, local smoothing filters, bilateral filtering, wavelet thresholding and median filtering, based on current researches. The main goals of this article are to recover the original image after applying a noise measurement, smoothing the images and preserves the edges. [ABSTRACT FROM AUTHOR]

Total variational (TV) methods using l 1 -norm are efficient approaches for optical flow determination. This contribution presents a multi-resolution TV- l 1 approach using a data-term based on neighborhood descriptors and a weighted non-local regularizer. The proposed algorithm is robust to illumination changes. The benchmarking of the proposed algorithm is done with three reference databases (Middlebury, KITTI and MPI Sintel). On these databases, the proposed approach exhibits an optimal compromise between robustness, accuracy and computation speed. Numerous tests performed both on complicated data of the reference databases and on challenging endoscopic images acquired under three different modalities demonstrate the robustness and accuracy of the method against the presence of large or small displacements, weak texture information, varying illumination conditions and modality changes. [ABSTRACT FROM AUTHOR]

This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement (called P), to mathematically model the eye fixations. P takes two criteria - the local consideration of surface curvature and the global consideration of viewindependent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approximate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface are important, is more sensible, especially when 3D models fall into two categories: (1) the models possess significant interior/exterior structures; (2) the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges. Edges are of critical importance to the visual appearance of images. So, it is desirable to preserve important features, such as edges, corners and other sharp structures, during the denoising process. This paper presents a review of some significant work in the area of image denoising. It provides a brief general classification of image denoising methods. The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising. It characterizes some of the well known edge-preserving denoising methods, elaborating each of them, and discusses the advantages and drawbacks of each. Basic ideas and improvement of the denoising methods are also comprehensively summarized and analyzed in depth. Often, researchers face difficulty in selecting an appropriate denoising method that is specific to their purpose. We have classified and systemized these denoising methods. The key goal of this paper is to provide researchers with background on a progress of denoising methods so as to make it easier for researchers to choose the method best suited to their aims. [ABSTRACT FROM AUTHOR]

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University 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.)

Wheezing is a common clinical symptom in patients with obstructive pulmonary diseases such as asthma. Automatic wheezing detection offers an objective and accurate means for identifying wheezing lung sounds, helping physicians in the diagnosis, long-term auscultation, and analysis of a patient with obstructive pulmonary disease. This paper describes the design of a fast and high-performance wheeze recognition system. A wheezing detection algorithm based on the order truncate average method and a back-propagation neural network (BPNN) is proposed. Some features are extracted from processed spectra to train a BPNN, and subsequently, test samples are analyzed by the trained BPNN to determine whether they are wheezing sounds. The respiratory sounds of 58 volunteers (32 asthmatic and 26 healthy adults) were recorded for training and testing. Experimental results of a qualitative analysis of wheeze recognition showed a high sensitivity of 0.946 and a high specificity of 1.0. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient method to remove haze from a single input image. Here, we presented an approach which is based on Fast Fourier Transform. Transmission map is refined by the dark channel prior method and Fast Fourier Transform. Finally the scene radiance is corrected using the visibility restoration model. Qualitative and quantitative results demonstrated that this method can effectively remove the bad weather condition and enhance the contrast of the input images and performs well in comparison with bilateral filtering. Moreover, the proposed method can significantly reduce the computational complexity. The use of Fast Fourier Transform in these images makes our approach faster by 88% in comparison to the bilateral filtering method. The main advantage of the proposed approach is suitable for images with too much of the sky background. Proposed method, due to its speed and ability to improve visibility, may be used in many systems such as surveillance, consumer electronics and remote sensing. [ABSTRACT FROM AUTHOR]

Noise removal from magnetic resonance images is important for further processing and visual analysis. Bilateral filter is known for its effectiveness in edge-preserved image denoising. In this paper, an iterative bilateral filter for filtering the Rician noise in the magnitude magnetic resonance images is proposed. The proposed iterative bilateral filter improves the denoising efficiency, preserves the fine structures and also reduces the bias due to Rician noise. The visual and diagnostic quality of the image is well preserved. The quantitative analysis based on the standard metrics like peak signal-to-noise ratio and mean structural similarity index matrix shows that the proposed method performs better than the other recently proposed denoising methods for MRI. [ABSTRACT FROM AUTHOR]

As a high-dimensional data, hyperspectral image contains rich information for agricultural remote sensing classification. Locality preserving projections (LPPs) have been widely used for extracting compact and discriminative information from such high-dimensional data. The objective function of LPP is formulated as a sum of the difference between transformed low dimensional vectors weighed by a function of the difference between images. The weights are crucial for LPP which enforce reduced feature vectors preserving the locality property in the original high dimensional space. In this paper, we borrow the idea of weight design of bilateral filtering to re-design the weights in LPP. The weights in bilateral filtering depend not only on the Euclidean distance of pixels (i.e., spatial weight) but also on the intensity differences (i.e., range weight). Analogously, we design the weights in our improved LPP (called bilateral LPP and abbreviated to BLPP) as a multiplication of a function of Euclidean distance ‖ x i − x j ‖ of the original images (i.e., spatial weight) and a function of the Euclidean distance ‖ f ( x i ) − f ( x j ) ‖ of the features extracted from the images (i.e., range weight, a.k.a., feature weight). The spatial weight measures the similarity in spatial space whereas the feature weight measures the similarity in feature space which reveals the content of the images. Thus, the proposed BLPP utilizes both the spatial information and the image content information, which results in higher recognition rate. Experimental results on the Salinas and Indian Pine hyperspectral databases demonstrate the effectiveness of BLPP. [ABSTRACT FROM AUTHOR]

Video conferences，a new type of conference in the internet age，have been widely used. Due to the influence of the external factors such as light, video acquisition device quality and network bandwidth, the quality of images of video conferences may deteriorate. Based on the multiple scale Retinex (MSR) algorithm, we divide the original images into illumination images of low frequency and high frequency reflection images by the bilateral filtering technique. And then the color constraints method and the gain compensation method are adopted to enhance the high frequency reflection images. Subjective experience and objective evaluation show that the improved algorithm enhances video images and preserves edge details when applied to soft video conferences, thus providing a better visual effect. [ABSTRACT FROM AUTHOR]

This paper formulates both the median filter and bilateral filter as a cost volume aggregation problem whose computational complexity is independent of the filter kernel size. Unlike most of the previous works, the proposed framework results in a general bilateral filter that can have arbitrary spatial $$^{1}$$ and arbitrary range filter kernels. This bilateral filter takes about 3.5 s to exactly filter a one megapixel 8-bit grayscale image on a 3.2 GHz Intel Core i7 CPU. In practice, the intensity/range and spatial domain can be downsampled to improve the efficiency. This compression can maintain very high accuracy (e.g., 40 dB) but over $$100\times $$ faster. [ABSTRACT FROM AUTHOR]

Range data acquired by affordable IR depth sensors are significantly noisy and lack shape detail, making it difficult to obtain high-quality scans using depth cameras. Most methods to enhance noisy data do not preserve the original structure of the measured scene during data refinement, particularly at sharp edges and object boundaries. We present a novel approach to enhance the range data in a structure-aware manner based on the normal information in a scene. We first computed reliable normals by smoothing out noise and sharpening the edges of individual points using color-guided normal smoothing. Based on the normal information, the noisy range data were refined by a 3D bilateral filtering technique while preserving ridges, valleys, and depth discontinuities. In a comparison, our method outperformed previous techniques in terms of noise suppression and faithful structure reconstruction. [ABSTRACT FROM AUTHOR]

A new method for fast bilateral filtering with texture-preserving properties is presented. In general, the texture area is composed of several tiny regions that have almost similar intensity, and therefore bilateral filtering combines similar gray scales of the texture area to form a smooth region. Adaptive boundary filtering solves this problem by using image segmentation to define a new weighting boundary at each pixel filtering. With the new type of boundary, the output image is smoothed while preserving both edges and textures details. The drawback of this algorithm is that it is time consuming. In this paper, a histogram-based filtering is proposed to improve the speed of this algorithm. However, as texture details are blurred when a small number of bins are used, it is unable to increase speed by using a small number of bins. Deciles-based algorithm is therefore applied to overcome this limitation, as it can increase the speed while preserving the texture details. The new algorithm, named fast adaptive boundary filtering, increases the speed by more than 40% when compared with adaptive boundary filtering at a boundary threshold T = 40. The output image of new algorithm shows that it is similar to the output of adaptive boundary filtering. Moreover, the speed of new algorithm is compared with the normal 256-bin histogram-based bilateral filtering. From the experimental results, it is seen that the new algorithm has higher speed when it is processed with a small to moderate radius window. In addition, a better output image can be obtained from the new algorithm as it preserves both edges and textures. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

One of the fastest acceleration techniques for bilateral image filtering is the real time O(1) quantization method proposed by Yang 2009, which first computes some Principal Bilateral Filtered Image Components (PBFICs) and then applies linear interpolation to estimate the filtered output images. There is a trade-off between accuracy and efficiency in selecting the number of PBFICs: the more PBFICs are used, the higher the accuracy, and the higher the computational cost. A question arises: how many PBFICs are required to achieve a certain level of accuracy? In this letter, we address this question by investigating the properties of bilateral filtering and deriving the linear interpolation error bounds when only a subset of PBFICs is used. The provided theoretical analysis indicates that the necessary number of PBFICs for user-provided precision depends on the range kernel and, for typical Gaussian range kernels, a small percentage (typically less than 4%) of the PBFICs are enough for good approximations. [ABSTRACT FROM AUTHOR]

Purpose: Detection of subtle microcalcifications in digital breast tomosynthesis (DBT) is a challenging task because of the large, noisy DBT volume. It is important to enhance the contrast-to-noise ratio (CNR) of microcalcifications in DBT reconstruction. Most regularization methods depend on local gradient and may treat the ill-defined margins or subtle spiculations of masses and subtle microcalcifications as noise because of their small gradient. The authors developed a new multiscale bilateral filtering (MSBF) regularization method for the simultaneous algebraic reconstruction technique (SART) to improve the CNR of microcalcifications without compromising the quality of masses. Methods: The MSBF exploits a multiscale structure of DBT images to suppress noise and selectively enhance high frequency structures. At the end of each SART iteration, every DBT slice is decomposed into several frequency bands viaLaplacian pyramid decomposition. No regularization is applied to the low frequency bands so that subtle edges of masses and structured background are preserved. Bilateral filtering is applied to the high frequency bands to enhance microcalcifications while suppressing noise. The regularized DBT images are used for updating in the next SART iteration. The new MSBF method was compared with the nonconvex total p-variation (TpV) method for noise regularization with SART. A GE GEN2 prototype DBT system was used for acquisition of projections at 21 angles in 3° increments over a ±30° range. The reconstruction image quality with no regularization (NR) and that with the two regularization methods were compared using the DBT scans of a heterogeneous breast phantom and several human subjects with masses and microcalcifications. The CNR and the full width at half maximum (FWHM) of the line profiles of microcalcifications and across the spiculations within their in-focus DBT slices were used as image quality measures. Results: The MSBF method reduced contouring artifacts and enhanced the CNR of microcalcifications compared to the TpV method, thus preserving the image quality of the structured background. The MSBF method achieved the highest CNR of microcalcifications among the three methods. The FWHM of the microcalcifications and mass spiculations resulting from the MSBF method was comparable to that without regularization, and superior to that of the TpV method. Conclusions: The SART regularized by the multiscale bilateral filtering method enhanced the CNR of microcalcifications and preserved the sharpness of microcalcifications and spiculated masses. The MSBF method provided better image quality of the structured background and was superior to TpV and NR for enhancing microcalcifications while preserving the appearance of mass margins. [ABSTRACT FROM AUTHOR]

Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries. [ABSTRACT FROM AUTHOR]

We present a stereo algorithm that is capable of estimating scene depth information with high accuracy and in real time. The key idea is to employ an adaptive cost-volume filtering stage in a dynamic programming optimization framework. The per-pixel matching costs are aggregated via a separable implementation of the bilateral filtering technique. Our separable approximation offers comparable edge-preserving filtering capability and leads to a significant reduction in computational complexity compared to the traditional 2D filter. This cost aggregation step resolves the disparity inconsistency between scanlines, which are the typical problem for conventional dynamic programming based stereo approaches. Our algorithm is driven by two design goals: real-time performance and high accuracy depth estimation. For computational efficiency, we utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this aggregation process over two orders of magnitude. Over 90 million disparity evaluations per second [the number of disparity evaluations per seconds (MDE/s) corresponds to the product of the number of pixels and the disparity range and the obtained frame rate and, therefore, captures the performance of a stereo algorithm in a single number] are achieved in our current implementation. In terms of quality, quantitative evaluation using data sets with ground truth disparities shows that our approach is one of the state-of-the-art real-time stereo algorithms. [ABSTRACT FROM AUTHOR]

Highlights: [•] Variance of Gaussian filter and step size are adjusted adaptively. [•] Leftward filter is added. [•] Prediction error is separated by its plus–minus sign. [•] Four images of prediction error are fused. [•] Clouds and noises are significantly suppressed. [Copyright &y& Elsevier]

Stereopsis is the basis for numerous tasks in machine vision, robotics, and 3D data acquisition and processing. In order for the subsequent algorithms to function properly, it is important that an affordable method exists that, given a pair of images taken by two cameras, can produce a representation of disparity or depth. This topic has been an active research field since the early days of work on image processing problems and rich literature is available on the topic. Joint bilateral filters have been recently proposed as a more affordable alternative to anisotropic diffusion. This class of image operators utilizes correlation in multiple modalities for purposes such as interpolation and upscaling. In this work, we develop the application of bilateral filtering for converting a large set of sparse disparity measurements into a dense disparity map. This paper develops novel methods for utilizing bilateral filters in joint, pyramid, and doubly joint settings, for purposes including missing value estimation and upscaling. We utilize images of natural and man-made scenes in order to exhibit the possibilities offered through the use of pyramid doubly joint bilateral filtering for stereopsis. Graphical abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]