Showing papers on "Bilateral filter published in 2008"

Edge-preserving decompositions for multi-scale tone and detail manipulation

Abstract: Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In many of these applications, it is important to control the spatial scale of the extracted details, and it is often desirable to manipulate details at multiple scales, while avoiding visual artifacts.In this paper we introduce a new way to construct edge-preserving multi-scale image decompositions. We show that current basedetail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Instead, we advocate the use of an alternative edge-preserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction. After describing this operator, we show how to use it to construct edge-preserving multi-scale decompositions, and compare it to the bilateral filter, as well as to other schemes. Finally, we demonstrate the effectiveness of our edge-preserving decompositions in the context of LDR and HDR tone mapping, detail enhancement, and other applications.

Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing

Abstract: We introduce a nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing. The approach considers the problem as a variational one, which consists of minimizing a weighted sum of two energy terms: a regularization one that uses a discrete weighted -Dirichlet energy and an approximation one. This is the discrete analogue of recent continuous Euclidean nonlocal regularization functionals. The proposed formulation leads to a family of simple and fast nonlinear processing methods based on the weighted -Laplace operator, parameterized by the degree of regularity, the graph structure and the graph weight function. These discrete processing methods provide a graph-based version of recently proposed semi-local or nonlocal processing methods used in image and mesh processing, such as the bilateral filter, the TV digital filter or the nonlocal means filter. It works with equal ease on regular 2-D and 3-D images, manifolds or any data. We illustrate the abilities of the approach by applying it to various types of images, meshes, manifolds, and data represented as graphs.

Multiresolution Bilateral Filtering for Image Denoising

Abstract: The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.

Bilateral filtering : theory and applications

Abstract: 1: Introduction 2: From Gaussian Convolution to Bilateral Filter 3: Applications 4: Efficient Implementation 5: Relationship between BF and Other Methods or Framework 6: Extensions of Bilateral Filtering 7: Conclusions. Acknowledgements. References.

Constant time O(1) bilateral filtering

Abstract: This paper presents three novel methods that enable bilateral filtering in constant time O(1) without sampling. Constant time means that the computation time of the filtering remains same even if the filter size becomes very large. Our first method takes advantage of the integral histograms to avoid the redundant operations for bilateral filters with box spatial and arbitrary range kernels. For bilateral filters constructed by polynomial range and arbitrary spatial filters, our second method provides a direct formulation by using linear filters of image powers without any approximation. Lastly, we show that Gaussian range and arbitrary spatial bilateral filters can be expressed by Taylor series as linear filter decompositions without any noticeable degradation of filter response. All these methods drastically decrease the computation time by cutting it down constant times (e.g. to 0.06 seconds per 1MB image) while achieving very high PSNRpsilas over 45 dB. In addition to the computational advantages, our methods are straightforward to implement.

Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal

Abstract: In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.

Real-time foreground segmentation via range and color imaging

Abstract: This paper describes a real-time method for foreground/background segmentation of a color video sequence based primarily on range data of a time-of-flight sensor. This method uses depth information of a TOF-sensor paired with a high resolution color video camera to efficiently segment foreground from background in a two-step process. First a trimap is produced using only range data: areas are located in each frame that have a high probability of being background or foreground, respectively. Pixels which cannot be definitively classified as foreground or background, typically about 1-2% of the frame, are assigned alpha-matte values using a cross bilateral filtering, applied directly to an estimate of the alpha-matte.

Edge-Preserving Smoothing and Mean-Shift Segmentation of Video Streams

Abstract: Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be efficient in order to process the data on the fly. From a theoretical standpoint, video streams have their own specificities --- they mix spatial and temporal dimensions, and compared to standard video sequences, half of the information is missing, i.e. the future is unknown. The theoretical part of our work is motivated by the ubiquitous use of the Gaussian kernel in tools such as bilateral filtering and mean-shift segmentation. We formally derive its equivalent for video streams as well as a dedicated expression of isotropic diffusion. Building upon this theoretical ground, we adapt a number of classical algorithms to video streams: bilateral filtering, mean-shift segmentation, and anisotropic diffusion.

Improved novel view synthesis from depth image with large baseline

Abstract: In this paper, a new algorithm is developed for recovering the large disocclusion regions in depth image based rendering (DIBR) systems on 3DTV. For the DIBR systems, undesirable artifacts occur in the disocclusion regions by using the conventional view synthesis techniques especially with large baseline. Three techniques are proposed to improve the view synthesis results. The first is the preprocessing of the depth image by using the bilateral filter, which helps to sharpen the discontinuous depth changes as well as to smooth the neighboring depth of similar color, thus restraining noises from appearing on the warped images. Secondly, on the warped image of a new viewpoint, we fill the disocclusion regions on the depth image with the background depth levels to preserve the depth structure. For the color image, we propose the depth-guided exemplar-based image inpainting that combines the structural strengths of the color gradient to preserve the image structure in the restored regions. Finally, a trilateral filter, which simultaneous combines the spatial location, the color intensity, and the depth information to determine the weighting, is applied to enhance the image synthesis results. Experimental results are shown to demonstrate the superior performance of the proposed novel view synthesis algorithm compared to the traditional methods.

Modeling a Generic Tone‐mapping Operator

Abstract: Although several new tone-mapping operators are proposed each year, there is no reliable method to validate their performance or to tell how different they are from one another. In order to analyze and understand the behavior of tone-mapping operators, we model their mechanisms by fitting a generic operator to an HDR image and its tone-mapped LDR rendering. We demonstrate that the majority of both global and local tone-mapping operators can be well approximated by computationally inexpensive image processing operations, such as a per-pixel tone curve, a modulation transfer function and color saturation adjustment. The results produced by such a generic tone-mapping algorithm are often visually indistinguishable from much more expensive algorithms, such as the bilateral filter. We show the usefulness of our generic tone-mapper in backward-compatible HDR image compression, the black-box analysis of existing tone mapping algorithms and the synthesis of new algorithms that are combination of existing operators.

MRI denoising using bilateral filter in redundant wavelet domain

Abstract: Thermal noise is the main source of noise in Magnetic Resonance Imaging (MRI) technique. The image is commonly reconstructed by computing inverse discrete Fourier transform of the raw data. The noise in the reconstructed complex data is complex white gaussian noise. The magnitude of the reconstructed magnetic resonance image is used for diagnosis and automatic computer analysis. An efficient method for enhancement of noisy magnetic resonance image using Bilateral filter in the undecimated wavelet domain is proposed. Undecimated Wavelet Transform (UDWT) is employed to provide effective representation of the noisy coefficients. The filter coefficients of the UDWT are not up sampled with increase in the level of decomposition. Applying bilateral filter on the transformed approximation coefficients, effectively preserves the relevant edge features and removes the noisy coefficients. The reconstructed MRI data has high peak signal to noise ratio (PSNR) compared to the classical wavelet domain denoising approaches.

A new image denoising method based on the bilateral filter

Abstract: In this paper we propose a new method to reduce noise in digital images. The method is based on the bilateral filter. The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. The spatial averaging aspect of the bilateral filter is very crucial; the bilateral filter has been shown to work better than wavelet thresholding in some recent papers. The proposed method improves the bilateral filter through decomposing a signal into its frequency components. In this way, noise in different frequency components can be eliminated. Experimental results with both simulated and real images are given. In addition to this new method, we also provide an empirical study of the optimal parameter selection for the bilateral filter.

Method for bilateral filtering of digital images

Abstract: A method for processing an image using a bilateral filter. The bilateral filter is reformulated at each pixel location in the image into a sum of the original signal value of a central pixel at said pixel location and a bilateral correction term which is a function of local signal differences between the central pixel and its neighbors. The bilateral correction term is calculated using a computationally efficient approximation.

Edge-Preserving Filtering of Images with Low Photon Counts

Abstract: Edge-preserving filters such as local M-smoothers or bilateral filtering are usually designed for Gaussian noise. This paper investigates how these filters can be adapted in order to efficiently deal with Poissonian noise. In addition, the issue of photometry invariance is addressed by changing the way filter coefficients are normalized. The proposed normalization is additive, instead of being multiplicative, and leads to a strong connection with anisotropic diffusion. Experiments show that ensuring the photometry invariance leads to comparable denoising performances in terms of the root mean square error computed on the signal.

Color mask for an image sensor of a vehicle camera

Abstract: A color mask for an image sensor of a vehicle camera has a matrix arrangement made up of first filter pixels and second filter pixels. In every horizontal row and/or every vertical column, first filter pixels and second filter pixels are situated, the first filter pixels and the second filter pixels having different transmission behaviors. The second filter pixels have a more comprehensive transmission behavior, e.g., completely transparent to optical light. The first filter pixels are preferably red.

Bilateral Filtering of fMRI Data

Abstract: We present a class of adaptive filtering techniques of functional magnetic resonance imaging (fMRI) data related to bilateral filtering. This class of methods average activities in consistent regions rather than regions that maximize correlation with a BOLD model. Similarity measures based on signal similarity and anatomical similarity are discussed and compared experimentally to standard linear low pass filtering. It is demonstrated that adaptive filtering provides improved detection of activated regions.

FPGA Implementation of Real-Time Edge-Preserving Filter for Video Noise Reduction

Abstract: In this paper, VLSI architectures and FPGA implementation for edge-preserving filter are presented. We proposed two architectures for edge preserving filter: full parallel pipelined and structure-shared architectures. The edge-preserving filter uses adaptive coefficient mask based on the intensity distance in filter blocks. Compared with the bilateral filter, the proposed edge-preserving filter provides significantly noise reduction. We implement the proposed architecture on Cyclone II EP2C70F896C8 FPGA device from Altera Corp. Our experiments show that the PSNR improvement is up to 5.3 dB for Gaussian noisy images.

Apparatus and method for improving image resolution using fuzzy motion estimation

Abstract: The subject matter discloses a method and apparatus for re-sampling a sequence of images in order to improve its resolution, fill-in missing pixels, or de-interlace it The method operates locally in the images to be processed, comparing pixel values of pixels surrounding the target pixel to pixel values of substantially the same locations in neighboring images The comparison results in assigning a weight value for each area compared with the area containing the reviewed pixel The pixel value of the reviewed pixel is updated as a function of multiplying pixel values of the areas by the weight assigned to each area In another embodiment, areas within the same image are compared to areas containing the reviewed pixel The subject matter also discloses two possible penalty functions for improving the resolution of images

Real-time feature-aware video abstraction

Abstract: This paper presents a novel feature-aware rendering system that automatically abstracts videos and images with the goal of improving the effectiveness of imagery for visual communication tasks. We integrate the bilateral grid to simplify regions of low contrast, which is faster than the separable approximation to the bilateral filter, and use a feature flow-guided anisotropic edge detection filter to enhance regions of high contrast. The edges detected in this paper are smoother, more coherent and stylistic than those of the isotropic difference-of-Gaussian filter. The presented algorithms are highly parallel, allowing a real-time performance on modern GPUs. The implementation of our approach is straightforward. Several experimental examples are given at the end of the paper to demonstrate the effectiveness of our approach.

Measurement-based and scalable deblock filtering of image data

Abstract: In a deblocking operation, pixel values within a first block of pixels are compared, and pixel values in the first block are also compared to pixel values in a second block of pixels that is adjacent to the first block. Based on the results of the comparisons, a digital deblocking filter and a region of interest can be selected, where the region of interest identifies a number of pixels in the first block and a number of pixels in the second block to which the selected filter is to be applied.

Scanning images for pornography

Abstract: Heuristic analysis of image is performed to detect pornographic content. Pixels of an image representing a flesh-tone are identified. A heuristic analysis of the image is performed to classify the image as being pornographic or not. The analysis uses measures of a set of predetermined characteristics of the identified pixels as a heuristic to indicate a likelihood that the identified pixels contain pornographic content or not. Particular characteristics used are: the thickness of a region of identified pixels; the area of regions of adjacent identified pixels; the flatness of regions of adjacent identified pixels; the distance of pixels from the centre of the image; the degree of texture of regions adjacent identified pixels; the likelihood of the identified pixels being flesh-tone, and the area of the identified pixels. The heuristic analysis is layered, comprising a plurality of tests, each test using the set of predetermined characteristics with differing degrees of significance attributed to each characteristic.

Sinogram smoothing with bilateral filtering for low-dose CT

Abstract: Optimal noise control is critical for dose reduction in CT. In this work, we investigated the use of a locally-adaptive method for noise reduction in low-dose CT. This method is based upon bilateral filtering, which smoothes the projection data using a weighted average in a local neighborhood, where the weights are determined according to both the spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus without significantly sacrificing the spatial resolution. It is closely related to anisotropic diffusion, but is significantly faster. More importantly, a CT noise model can be readily incorporated in the filtering and denoising process. We have evaluated the noise-resolution properties of the bilateral filtering in a phantom study and a preliminary patient study with contrast-enhanced abdominal CT exams. The results demonstrated that bilateral filtering can achieve a better noise-resolution tradeoff than a series of commercial reconstruction kernels. This improvement on noise-resolution properties can be used for improving the image quality in low-dose CT and can also be translated to substantial dose reduction.

Image error-concealment via Block-based Bilateral Filtering

Abstract: We propose to use Block-based Bilateral Filter (BBF), which extends the classical Bilateral Filter (BF) through operating in block-wise manner, to conceal missing image blocks in the application of compressed image transmission over wireless channels. We show that the problem of error-concealment using BBF can be considered as a superset of image denoising using BF. The BBF has the ability to capture the block-level similarity that well matches the need of error-concealment for block based image compression. Simulation results suggest significant visual and PSNR improvements (up to 11 dB) over various classic and state-of-the-art error-concealment algorithms.

Shot noise adaptive bilateral filter

Abstract: The bilateral filter (BF) is an important image denoising local filter. It reduces noise in images while preserving edges by means of nonlinear combination of local pixel values. Its formulation and implementation are both simple. However, the BF is not parameter-free. The set of the bilateral filter parameters has an important influence on its behavior and performance. They have to be chosen considering the end application. In the case of noise removal, the parameters have to be adapted to the noise level, while the bilateral filter adapts itself to the image details content. In this paper we propose a method to estimate the best bilateral filter intensity parameter set in the case of shot noise removal, the dominant form of natural noise in digital imaging.

Image Diffusion Using Saliency Bilateral Filter

Abstract: Image diffusion can smooth away noise and small-scale structures while retaining important features, thus improving the performances for many image processing algorithms. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries. The effectiveness of the proposed approach is validated by experiments on various medical images.

A Novel Bio-inspired Algorithm for Color Image Enhancement

Abstract: We propose a novel bio-inspired algorithm to enhance the color image under low or non-uniform lighting conditions that models global and local adaptation of the human visual system.The proposed method consists of three parts:a preliminary global luminance adjustment followed by local contrast enhancement and color restoration.The global luminance adjustment increases the luminance of darker pixels and compresses the dynamic range as well.The local contrast enhancement adjusts the intensity of each pixel based on its relative magnitude with respect to the bilateral filter output of its neighboring pixels.Then a linear color restoration process is applied to convert the enhanced intensity image back to a color image.Experimental results of the proposed method and reference are compared and analyzed to illustrate the effectiveness of the proposed method.

Adaptive sharpening of photos

Abstract: Sharpness is an important attribute that contributes to the overall impression of printed photo quality. Often it is impossible to estimate sharpness prior to printing. Sometimes it is a complex task for a consumer to obtain accurate sharpening results by editing a photo on a computer. The novel method of adaptive sharpening aimed for photo printers is proposed. Our approach includes 3 key techniques: sharpness level estimation, local tone mapping and boosting of local contrast. Non-reference automatic sharpness level estimation is based on analysis of variations of edges histograms, where edges are produced by high-pass filters with various kernel sizes, array of integrals of logarithm of edges histograms characterizes photo sharpness, machine learning is applied to choose optimal parameters for given printing size and resolution. Local tone mapping with ordering is applied to decrease edge transition slope length without noticeable artifacts and with some noise suppression. Unsharp mask via bilateral filter is applied for boosting of local contrast. This stage does not produce strong halo artifact which is typical for the traditional unsharp mask filter. The quality of proposed approach is evaluated by surveying observer's opinions. According to obtained replies the proposed method enhances the majority of photos.

Variably fast and continuous bilateral approximation filtering using histogram manipulations

Abstract: A system and method for performing integral histogram convolution for filtering image data is disclosed. The method may include applying a filter window to a first portion of an image, wherein the filter window includes an interior region and a border region. The method may include generating a plurality of histograms for the pixels in the filter window. The method may include generating spatial weight coefficients for the pixels in the border of the filter window. The method may include generating a plurality of color weight coefficients for the pixels in the filter window. The method may include performing a filtering operation on the pixels in the filter window by applying a respective spatial weight coefficient and a respective color weight coefficient to the values in the plurality of histograms for each respective pixel in the filter window. The methods may be implemented by program instructions executing in parallel on CPU(s) or GPUs.

Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping

Abstract: This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.

Method and apparatus for reducing block noise

Abstract: Block noises at block boundaries are removed, but when a contour line in the original image matches a block boundary, the degree of block noise removal is adjusted and image quality is improved. An adaptive filter has a step of calculating the weight of pixels near the block boundary by a high-pass filter, and a step of determining an adjusted value for the pixels near the block boundary based on the weight. The pixel values near the block boundary are adjusted and block noises are removed. A low-pass filter is applied to pixels near the block boundary to determine a filtered value. The adjusted values of the pixels near the block boundary are determined by obtaining a weighted sum of the original pixel value and the filtered value. Because the high-pass filter recognizes the definition of pixels near the block boundary, and the low-pass filter reduces the difference between pixels near the block boundary, the adaptive filter outputs the adjusted value, and this value removes block noise while retaining image definition.