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Showing papers on "Bilateral filter published in 2011"


Proceedings ArticleDOI
25 Jul 2011
TL;DR: The use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization.
Abstract: We present a new approach for performing high-quality edge-preserving filtering of images and videos in real time. Our solution is based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line. This transform preserves the geodesic distance between points on these curves, adaptively warping the input signal so that 1D edge-preserving filtering can be efficiently performed in linear time. We demonstrate three realizations of 1D edge-preserving filters, show how to produce high-quality 2D edge-preserving filters by iterating 1D-filtering operations, and empirically analyze the convergence of this process. Our approach has several desirable features: the use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization. We demonstrate the versatility of our domain transform and edge-preserving filters on several real-time image and video processing tasks including edge-preserving filtering, depth-of-field effects, stylization, recoloring, colorization, detail enhancement, and tone mapping.

738 citations


Journal ArticleDOI
TL;DR: This paper focuses on first order features, i.e., facet normals, and presents a simple yet effective anisotropic mesh denoising framework via normal field Denoising, which considers normals as a surface signal defined over the original mesh.
Abstract: Decoupling local geometric features from the spatial location of a mesh is crucial for feature-preserving mesh denoising. This paper focuses on first order features, i.e., facet normals, and presents a simple yet effective anisotropic mesh denoising framework via normal field denoising. Unlike previous denoising methods based on normal filtering, which process normals defined on the Gauss sphere, our method considers normals as a surface signal defined over the original mesh. This allows the design of a novel bilateral normal filter that depends on both spatial distance and signal distance. Our bilateral filter is a more natural extension of the elegant bilateral filter for image denoising than those used in previous bilateral mesh denoising methods. Besides applying this bilateral normal filter in a local, iterative scheme, as common in most of previous works, we present for the first time a global, noniterative scheme for an isotropic denoising. We show that the former scheme is faster and more effective for denoising extremely noisy meshes while the latter scheme is more robust to irregular surface sampling. We demonstrate that both our feature-preserving schemes generally produce visually and numerically better denoising results than previous methods, especially at challenging regions with sharp features or irregular sampling.

224 citations


Journal ArticleDOI
TL;DR: In this article, a trigonometric range kernel was proposed to realize the bilateral filter in constant time, which is done by generalizing the idea presented by Porikli, i.e., using polynomial kernels.
Abstract: It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image-processing literature. Along with the spatial filter, the edge-preserving bilateral filter involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant time, by using trigonometric range kernels. This is done by generalizing the idea presented by Porikli, i.e., using polynomial kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained by Porikli using polynomials.

223 citations


Proceedings ArticleDOI
18 Nov 2011
TL;DR: This work presents a combined local-global (CLG) approach with total variation regularization that is able to compute larger displacements in a reasonable time and runs in real-time on current generation graphic processing units.
Abstract: More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.

116 citations


Journal ArticleDOI
TL;DR: The proposed spatial and temporal target detection method using spatial bilateral filter (BF) and temporal cross product (TCP) of temporal pixels in infrared (IR) image sequences shows better discrimination of target and clutters and lower false alarm rates than the existing target detection methods.

90 citations


Proceedings ArticleDOI
22 May 2011
TL;DR: This paper is among the first to propose a single-frame-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis (MCA) and demonstrates the efficacy of the proposed algorithm.
Abstract: Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image has been rarely studied in the literature, where no temporal information among successive images can be exploited, making it more challenging. In this paper, to the best of our knowledge, we are among the first to propose a single-frame-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis (MCA). Instead of directly applying conventional image decomposition technique, we first decompose an image into the low-frequency and high-frequency parts using a bilateral filter. The high-frequency part is then decomposed into “rain component” and “non-rain component” via performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.

87 citations


Journal ArticleDOI
TL;DR: A new method of display and detail enhancement for high dynamic range infrared images is presented that effectively maps the raw acquired infrared image to 8-bit domain based on the same architecture of bilateral filter and dynamic range partitioning approach.
Abstract: Dynamic range reduction and detail enhancement are two important issues for effectively displaying high-dynamic-range images acquired by thermal camera systems. They must be performed in such a way that the high dynamic range image signal output from sensors is compressed in a pleasing manner for display on lower dynamic range monitors without reducing the perceptibility of small details. In this paper, a new method of display and detail enhancement for high dynamic range infrared images is presented. This method effectively maps the raw acquired infrared image to 8-bit domain based on the same architecture of bilateral filter and dynamic range partitioning approach. It includes three main steps: First, a bilateral filter is applied to separate the input image into the base component and detail component. Second, refine the base and detail layer using an adaptive Gaussian filter to avoid unwanted artifacts. Then the base layer is projected to the display range and the detail layer is enhanced using an adaptive gain control approach. Finally, the two parts are recombined and quantized to 8-bit domain. The strength of the proposed method lies in its ability to avoid unwanted artifacts and adaptability in different scenarios. Its great performance is validated by the experimental results tested with two real infrared imagers.

78 citations


Proceedings ArticleDOI
11 Jul 2011
TL;DR: This paper proposes the first local method which is both fast (real-time) and produces results comparable to global algorithms, and uses the recently proposed guided filter to overcome the limitation of bilateral filtering.
Abstract: Adaptive support weight algorithms represent the state-of-the-art in local stereo matching. Their limitation is a high computational demand, which makes them unattractive for many (real-time) applications. To our knowledge, the algorithm proposed in this paper is the first local method which is both fast (real-time) and produces results comparable to global algorithms. A key insight is that the aggregation step of adaptive support weight algorithms is equivalent to smoothing the stereo cost volume with an edge-preserving filter. From this perspective, the original adaptive support weight algorithm [1] applies bilateral filtering on cost volume slices, and the reason for its poor computational behavior is that bilateral filtering is a relatively slow process. We suggest to use the recently proposed guided filter [2] to overcome this limitation. Analogously to the bilateral filter, this filter has edge-preserving properties, but can be implemented in a very fast way, which makes our stereo algorithm independent of the size of the match window. The GPU implementation of our stereo algorithm can process stereo images with a resolution of 640 × 480 pixels and a disparity range of 26 pixels at 25 fps. According to the Middlebury on-line ranking, our algorithm achieves rank 14 out of over 100 submissions and is not only the best performing local stereo matching method, but also the best performing real-time method.

78 citations


Journal ArticleDOI
TL;DR: A time-intensity profile similarity (TIPS) bilateral filter is proposed to reduce noise in 4D CTP scans, while preserving the time- intensity profiles (fourth dimension) that are essential for determining the perfusion parameters.
Abstract: Cerebral computed tomography perfusion (CTP) scans are acquired to detect areas of abnormal perfusion in patients with cerebrovascular diseases. These 4D CTP scans consist of multiple sequential 3D CT scans over time. Therefore, to reduce radiation exposure to the patient, the amount of x-ray radiation that can be used per sequential scan is limited, which results in a high level of noise. To detect areas of abnormal perfusion, perfusion parameters are derived from the CTP data, such as the cerebral blood flow (CBF). Algorithms to determine perfusion parameters, especially singular value decomposition, are very sensitive to noise. Therefore, noise reduction is an important preprocessing step for CTP analysis. In this paper, we propose a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in 4D CTP scans, while preserving the time-intensity profiles (fourth dimension) that are essential for determining the perfusion parameters. The proposed TIPS bilateral filter is compared to standard Gaussian filtering, and 4D and 3D (applied separately to each sequential scan) bilateral filtering on both phantom and patient data. Results on the phantom data show that the TIPS bilateral filter is best able to approach the ground truth (noise-free phantom), compared to the other filtering methods (lowest root mean square error). An observer study is performed using CBF maps derived from fifteen CTP scans of acute stroke patients filtered with standard Gaussian, 3D, 4D and TIPS bilateral filtering. These CBF maps were blindly presented to two observers that indicated which map they preferred for (1) gray/white matter differentiation, (2) detectability of infarcted area and (3) overall image quality. Based on these results, the TIPS bilateral filter ranked best and its CBF maps were scored to have the best overall image quality in 100% of the cases by both observers. Furthermore, quantitative CBF and cerebral blood volume values in both the phantom and the patient data showed that the TIPS bilateral filter resulted in realistic mean values with a smaller standard deviation than the other evaluated filters and higher contrast-to-noise ratios. Therefore, applying the proposed TIPS bilateral filtering method to 4D CTP data produces higher quality CBF maps than applying the standard Gaussian, 3D bilateral or 4D bilateral filter. Furthermore, the TIPS bilateral filter is computationally faster than both the 3D and 4D bilateral filters.

76 citations


Journal ArticleDOI
TL;DR: An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction that consistently performed better than the conventional FCM, MFCM, and MsFCM methods.
Abstract: Purpose: Classification of magnetic resonance (MR) images has many clinical and research applications. Because of multiple factors such as noise, intensity inhomogeneity, and partial volume effects, MR image classification can be challenging. Noise in MRI can cause the classified regions to become disconnected. Partial volume effects make the assignment of a single class to one region difficult. Because of intensity inhomogeneity, the intensity of the same tissue can vary with respect to the location of the tissue within the same image. The conventional “hard” classification method restricts each pixel exclusively to one class and often results in crisp results. Fuzzy C-mean (FCM) classification or “soft” segmentation has been extensively applied to MR images, in which pixels are partially classified into multiple classes using varying memberships to the classes. Standard FCM, however, is sensitive to noise and cannot effectively compensate for intensity inhomogeneities. This paper presents a method to obtain accurate MR brain classification using a modified multiscale and multiblock FCM. Methods: An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and by reducing the standard deviation of range function. At each scale, we separate the image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels in order to overcome the effect of intensity inhomogeneity. The result from a coarse scale supervises the classification in the next fine scale. The classification method is tested with noisy MR images with intensity inhomogeneity. Results: Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method. Validation studies were performed on synthesized images with various contrasts, on the simulated brain MR database, and on real MR images. Our MsbFCM method consistently performed better than the conventional FCM, MFCM, and MsFCM methods. The MsbFCM method achieved an overlap ratio of 91% or higher. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images. Conclusions: As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications.

61 citations


Journal ArticleDOI
TL;DR: A new scene-based NUC technique based on bilateral filter has been developed that separates the original input frames into two parts and it estimates the NUC parameters only by using the residuals.
Abstract: A thorough analysis of low convergence speed and ghosting artifacts in temporal high-pass filter correction has been undertaken in this paper and it has found out that the keys of these problems are the interference of a large sum of unrelated scene information in the nonuniformity correction (NUC) process. In order to overcome these drawbacks, a new scene-based NUC technique based on bilateral filter has been developed. This method separates the original input frames into two parts and it estimates the NUC parameters only by using the residuals. The experimental results have shown that it can significantly increase convergence speed and reduce ghosting artifacts.

Journal ArticleDOI
TL;DR: Bilateral filtering allows to increase the SNR of PET image data while preserving spatial resolution and preventing smoothing-induced underestimation of SUVmax values in small lesions and seems a promising and superior alternative to standard smoothing filters.
Abstract: To achieve an acceptable signal-to-noise ratio (SNR) in PET images, smoothing filters (SF) are usually employed during or after image reconstruction preventing utilisation of the full intrinsic resolution of the respective scanner. Quite generally Gaussian-shaped moving average filters (MAF) are used for this purpose. A potential alternative to MAF is the group of so-called bilateral filters (BF) which provide a combination of noise reduction and edge preservation thus minimising resolution deterioration of the images. We have investigated the performance of this filter type with respect to improvement of SNR, influence on spatial resolution and for derivation of SUVmax values in target structures of varying size. Data of ten patients with head and neck cancer were evaluated. The patients had been investigated by routine whole body scans (ECAT EXACT HR+, Siemens, Erlangen). Tomographic images were reconstructed (OSEM 6i/16s) using a Gaussian filter (full width half maximum (FWHM): Γ0 = 4 mm). Image data were then post-processed with a Gaussian MAF (FWHM: ΓM = 7 mm) and a Gaussian BF (spatial domain: ΓS = 9 mm, intensity domain: ΓI = 2.5 SUV), respectively. Images were assessed regarding SNR as well as spatial resolution. Thirty-four lesions (volumes of about 1-100 mL) were analysed with respect to their SUVmax values in the original as well as in the MAF and BF filtered images. With the chosen filter parameters both filters improved SNR approximately by a factor of two in comparison to the original data. Spatial resolution was significantly better in the BF-filtered images in comparison to MAF (MAF: 9.5 mm, BF: 6.8 mm). In MAF-filtered data, the SUVmax was lower by 24.1 ± 9.9% compared to the original data and showed a strong size dependency. In the BF-filtered data, the SUVmax was lower by 4.6 ± 3.7% and no size effects were observed. Bilateral filtering allows to increase the SNR of PET image data while preserving spatial resolution and preventing smoothing-induced underestimation of SUVmax values in small lesions. Bilateral filtering seems a promising and superior alternative to standard smoothing filters.

Journal ArticleDOI
TL;DR: This paper presents a fast implementation of the bilateral filter with arbitrary range and domain kernels based on the fast bilateral filter approximation that uses uniform box domain kernel.
Abstract: In this paper, we present a fast implementation of the bilateral filter with arbitrary range and domain kernels. It is based on the histogram-based fast bilateral filter approximation that uses uniform box as the domain kernel. Instead of using a single box kernel, multiple box kernels are used and optimally combined to approximate an arbitrary domain kernel. The method achieves better approximation of the bilateral filter compared to the single box kernel version with little increase in computational complexity. We also derive the optimal kernel size when a single box kernel is used.

Book ChapterDOI
20 Nov 2011
TL;DR: Experiments showed the results produced from the proposed adaptive guided image filtering (AGF) are superior to those produced from unsharp masking-based techniques and comparable to ABF filtered output.
Abstract: Sharpness enhancement and noise reduction play crucial roles in computer vision and image processing. The problem is to enhance the appearance and reduce the noise of the digital images without causing halo artifacts. In this paper, we propose an adaptive guided image filtering (AGF) able to perform halo-free edge slope enhancement and noise reduction simulaneously. The proposed method is developed based on guided image filtering (GIF) and the shift-variant technique, part of adaptive bilateral filtering (ABF). Experiments showed the results produced from our method are superior to those produced from unsharp masking-based techniques and comparable to ABF filtered output. Our proposed AGF outperforms ABF in terms of computational complexity. It is implemented using a fast and exact linear-time algorithm.

Patent
16 Dec 2011
TL;DR: In this article, a first filter decision value is calculated for a block (10) of pixels (11, 13, 15, 17) in a video frame based on pixel values of pixels(21, 23, 25, 27, 27), in a corresponding first line (22), where pixels (21,23, 25 and 27) are in a neighboring block (20) in the video frame.
Abstract: A first filter decision value is calculated for a block (10) of pixels (11, 13, 15, 17) in a video frame based on pixel values of pixels (11, 13, 15) in a first line (12) of pixels (11, 13, 15, 17) in the block (10) A second filter decision value is also calculated for the block (10) based on pixel values of pixels (21, 23, 25, 27) in a corresponding first line (22) of pixels (21, 23, 25, 27) in a neighboring block (20) in the video frame The first filter decision value is used to determine how many pixels in a line (12) of pixels (11, 13, 15, 17) in the block (10) to filter relative to a block boundary (1) between the block (10) and the neighboring block (20) The second filter decision value is used to determine how many pixels in a corresponding line (22) of pixels (21, 23, 25, 27) in the neighboring block to filter relative to the block boundary (1)

Journal ArticleDOI
15 Mar 2011-Sensors
TL;DR: Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.
Abstract: This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.

Proceedings ArticleDOI
TL;DR: In this paper, a bilateral filter consisting of two (domain and range) filter kernels is replaced with a smoothing filter that conforms to image structures, which is suitable for seismic image processing.
Abstract: Bilateral filtering is widely used to enhance photographic images, but in most implementations is poorly suited to seismic images. A bilateral filter consists of two (domain and range) filter kernels. By replacing the domain kernel with a smoothing filter that conforms to image structures, we obtain a bilateral filter suitable for seismic image processing. Examples and comparison with conventional edge-preserving smoothing illustrate advantages of structure-oriented bilateral filtering. The only significant disadvantage is a relatively high (roughly 10 to 40 times higher) computational cost.

Journal ArticleDOI
TL;DR: This letter identifies a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations and shows how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images.
Abstract: It was recently demonstrated in [5] that the non-linear bilateral filter [14] can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [5] can be extended to few other linear and non-linear filters [14, 17, 2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in [5], we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum [15, 6], and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (non-shiftable) Gaussian kernel that is ubiquitously used in image filtering.

Patent
28 Jan 2011
TL;DR: In this article, a joint bilateral filter is applied to a first depth map to generate a second depth map, where at least one filter weight is adapted based upon content of an image represented by the first depth image.
Abstract: A method and apparatus for generating a dense depth map. In one embodiment, the method includes applying a joint bilateral filter to a first depth map to generate a second depth map, where at least one filter weight of the joint bilateral filter is adapted based upon content of an image represented by the first depth map, and the second depth map has a higher resolution than the first depth map.

Book ChapterDOI
Jesús Angulo1
06 Jul 2011
TL;DR: A new low complexity approach to define spatially-variant bilateral structuring functions is proposed, starting from the bilateral filtering framework and using the notion counter-harmonic mean to introduce adaptive nonlinear filters which asymptotically correspond to spatiallyvariant morphological dilation and erosion.
Abstract: Development of spatially-variant filtering is well established in the theory and practice of Gaussian filtering. The aim of the paper is to study how to generalize these linear approaches in order to introduce adaptive nonlinear filters which asymptotically correspond to spatiallyvariant morphological dilation and erosion. In particular, starting from the bilateral filtering framework and using the notion counter-harmonic mean, our goal is to propose a new low complexity approach to define spatially-variant bilateral structuring functions. Then, the adaptive structuring elements are obtained by thresholding the bilateral structuring functions. The methodological results of the paper are illustrated with various comparative examples.

Proceedings ArticleDOI
29 Dec 2011
TL;DR: Cosine integral images (CII) as discussed by the authors represent a large set of spatial and range filters, based on their frequency decomposition The filtering requires a constant number of operations per image pixel, independent of filter size.
Abstract: Non uniform kernels is important for many image processing algorithms However, for large kernel sizes the filtering can become computationally expensive We introduce cosine integral images (CII) which represent a large set of spatial and range filters, based on their frequency decomposition The filtering requires a constant number of operations per image pixel, independent of filter size We make use of CII to compute the Gabor filters, whose complexity is for the first time a constant O(1) operations per image pixel We also improve previous constant time approximations of spatial Gaussian smoothing and bilateral filtering

Proceedings ArticleDOI
30 Aug 2011
TL;DR: The proposed filter is an extension the pixel weighted average strategy for depth sensor data fusion, which includes a new factor that allows to adaptively consider 2-D data or 3-DData as guidance information, and outperforming alternative depth enhancement filters.
Abstract: We present an adaptive multi-lateral filter for real-time low-resolution depth map enhancement. Despite the great advantages of Time-of-Flight cameras in 3-D sensing, there are two main drawbacks that restricts their use in a wide range of applications; namely, their fairly low spatial resolution, compared to other 3-D sensing systems, and the high noise level within the depth measurements. We therefore propose a new data fusion method based upon a bilateral filter. The proposed filter is an extension the pixel weighted average strategy for depth sensor data fusion. It includes a new factor that allows to adaptively consider 2-D data or 3-D data as guidance information. Consequently, unwanted artefacts such as texture copying get almost entirely eliminated, outperforming alternative depth enhancement filters. In addition, our algorithm can be effectively and efficiently implemented for real-time applications.

Journal ArticleDOI
TL;DR: This paper describes a cluster-based method for combining differently exposed images in order to increase their dynamic range and allows recovering details from overexposed and underexposed parts of image without producing additional noise.

Proceedings ArticleDOI
29 Dec 2011
TL;DR: A “High Dynamic Range (HDR) Filter” is described that can mitigate these artifacts to produce a pleasing HDR video without exact frame registration and shows a significant improvement for HDR videos with fast local motion within saturated regions.
Abstract: One method to extend the dynamic range of video captured with inexpensive cameras is to alternate the exposure time between frames and combine the information in adjacent frames using post-processing. This method requires no hardware modification, yet traditionally there is a quality tradeoff. Dynamic range expansion corresponds to an increased number of saturated pixels in individual frames, which along with occlusions contributes to registration artifacts. Therefore, we describe a “High Dynamic Range (HDR) Filter” that can mitigate these artifacts to produce a pleasing HDR video without exact frame registration. This filter builds upon the bilateral filter to smooth frames while maintaining important edges. Additionally, the filter strength locally adapts to corresponding motion vectors. Since regions with poor registration generally correspond to higher motion, smoothing here can reduce artifacts without degrading perceptual quality. Results show a significant improvement for HDR videos with fast local motion within saturated regions.

Proceedings ArticleDOI
19 Apr 2011
TL;DR: Comparison with other methods, such as nonlinear diffusion, Fourth-Order Partial Differential Equations, Total variation, Nonlocal mean, Wavelet thresholding, and Bilateral filters, shows that the proposed multi resolution bilateral filter (MRBF) produces better denoising results.
Abstract: Clinical magnetic resonance imaging (MRI) data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to increase the : Signal-to-Noise Ratio (SNR) and improve image quality. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. In this paper we propose an extension of the bilateral filter: multi resolution bilateral filter (MRBF), with wavelet transform (WT) sub-bands mixing. The proposed wavelet sub-bands mixing is based on a multi resolution approach for improving the quality of image denoising filter, which turns out to be very effective in eliminating noise in noisy images. Quantitative validation was carried out on synthetic datasets generated with the Brain Web simulator. Comparison with other methods, such as nonlinear diffusion, Fourth-Order Partial Differential Equations, Total variation, Nonlocal mean, Wavelet thresholding, and Bilateral filters, shows that the proposed multi resolution bilateral filter (MRBF) produces better denoising results. The mathematical analysis is based on the analysis of the "method noise", defined as the difference between a digital image and its denoised version. The MRBF algorithm is also proven to be asymptotically optimal under a generic statistical image model. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

Journal ArticleDOI
TL;DR: The proposed method for removing noise from digital images, based on bilateral filter and Gaussian scale mixtures in shiftable complex directional pyramid domain, can preserve edges very well while removing noise.

Journal ArticleDOI
TL;DR: In this article, it was shown that the nonlinear bilateral filter can be implemented in O(1) time using trigonometric functions, which can be extended to a few other linear and nonlinear filters.
Abstract: It was recently demonstrated in that the nonlinear bilateral filter can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in can be extended to few other linear and nonlinear filters . While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in , we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum , , and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (nonlinearshiftable) Gaussian kernel that is ubiquitously used in image filtering.

Proceedings ArticleDOI
01 Nov 2011
TL;DR: This work addresses the problem of sharpness enhancement of images by proposing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter, whereas techniques based on weighted least squares extract low contrast features as detail.
Abstract: We address the problem of sharpness enhancement of images. Existing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter suffer from halo effects, whereas techniques based on weighted least squares extract low contrast features as detail. Other techniques require multiple images and are not tolerant to noise.

Proceedings ArticleDOI
12 Dec 2011
TL;DR: This algorithm is an adaptive upsampling filter that takes into account the inherent noisy nature of depth data and can improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry.
Abstract: Depth maps are used in many applications, e.g. 3D television, stereo matching, segmentation, etc. Recently, a new generation of active 3D range sensors, such as time-of-flight (TOF) cameras, enables recording of full frame depth maps at video frame rate. Unfortunately, depth maps captured with the TOF cameras have limited resolution and poor image quality, being serverely influenced by the random and systematic noise, which makes them innaposite for generating high quality 3D images. In this paper, we proposed a method to enhance the quality and increase the spatial resolution of range data by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive upsampling filter that takes into account the inherent noisy nature of depth data. Thus, we can improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry.

Posted Content
TL;DR: This paper shows how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant time, by using trigonometric range kernels, by generalizing the idea presented by Porikli, i.e., using polynomial kernels.
Abstract: It is well-known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not depend on the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image processing literature. Along with the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process non-linear and computationally intensive, especially when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant-time, by using trigonometric range kernels. This is done by generalizing the idea in [Porikli2008] of using polynomial range kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained in [Porikli2008] using polynomials.