Showing papers on "Bilateral filter published in 2014"

Bilateral texture filtering

Abstract: This paper presents a novel structure-preserving image decomposition operator called bilateral texture filter. As a simple modification of the original bilateral filter [Tomasi and Manduchi 1998], it performs local patch-based analysis of texture features and incorporates its results into the range filter kernel. The central idea to ensure proper texture/structure separation is based on patch shift that captures the texture information from the most representative texture patch clear of prominent structure edges. Our method outperforms the original bilateral filter in removing texture while preserving main image structures, at the cost of some added computation. It inherits well-known advantages of the bilateral filter, such as simplicity, local nature, ease of implementation, scalability, and adaptability to other application scenarios.

Fast Local Laplacian Filters: Theory and Applications

Abstract: Multiscale manipulations are central to image editing but also prone to halos. Achieving artifact-free results requires sophisticated edge-aware techniques and careful parameter tuning. These shortcomings were recently addressed by the local Laplacian filters, which can achieve a broad range of effects using standard Laplacian pyramids. However, these filters are slow to evaluate and their relationship to other approaches is unclear. In this article, we show that they are closely related to anisotropic diffusion and to bilateral filtering. Our study also leads to a variant of the bilateral filter that produces cleaner edges while retaining its speed. Building upon this result, we describe an acceleration scheme for local Laplacian filters on gray-scale images that yields speedups on the order of 50×. Finally, we demonstrate how to use local Laplacian filters to alter the distribution of gradients in an image. We illustrate this property with a robust algorithm for photographic style transfer.

Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree

Abstract: We present a new efficient edge-preserving filter-“tree filter”-to achieve strong image smoothing. The proposed filter can smooth out high-contrast details while preserving major edges, which is not achievable for bilateral-filter-like techniques. Tree filter is a weighted-average filter, whose kernel is derived by viewing pixel affinity in a probabilistic framework simultaneously considering pixel spatial distance, color/intensity difference, as well as connectedness. Pixel connectedness is acquired by treating pixels as nodes in a minimum spanning tree (MST) extracted from the image. The fact that an MST makes all image pixels connected through the tree endues the filter with the power to smooth out high-contrast, fine-scale details while preserving major image structures, since pixels in small isolated region will be closely connected to surrounding majority pixels through the tree, while pixels inside large homogeneous region will be automatically dragged away from pixels outside the region. The tree filter can be separated into two other filters, both of which turn out to have fast algorithms. We also propose an efficient linear time MST extraction algorithm to further improve the whole filtering speed. The algorithms give tree filter a great advantage in low computational complexity (linear to number of image pixels) and fast speed: it can process a 1-megapixel 8-bit image at ~ 0.25 s on an Intel 3.4 GHz Core i7 CPU (including the construction of MST). The proposed tree filter is demonstrated on a variety of applications.

Edge guided single depth image super resolution

Abstract: Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and quality of the depth map generated by these cameras are still problems for several applications. In this paper, we propose a novel framework for single depth image super resolution guided by a high resolution edge map constructed from the edges in the low resolution depth image via a Markov Random Field (MRF) optimization. With the guidance of the high resolution edge map, the high resolution depth image is up-sampled via a joint bilateral filter. The edge guidance not only helps avoid artifacts introduced by direct texture prediction, but also reduces the jagged artifacts and preserves the sharp edges. Experimental results demonstrate the effectiveness of our proposed algorithm compared to previously reported methods.

Selectively Detail-Enhanced Fusion of Differently Exposed Images With Moving Objects

Abstract: In this paper, we introduce an exposure fusion scheme for differently exposed images with moving objects. The proposed scheme comprises a ghost removal algorithm in a low dynamic range domain and a selectively detail-enhanced exposure fusion algorithm. The proposed ghost removal algorithm includes a bidirectional normalization-based method for the detection of nonconsistent pixels and a two-round hybrid method for the correction of nonconsistent pixels. Our detail-enhanced exposure fusion algorithm includes a content adaptive bilateral filter, which extracts fine details from all the corrected images simultaneously in gradient domain. The final image is synthesized by selectively adding the extracted fine details to an intermediate image that is generated by fusing all the corrected images via an existing multiscale algorithm. The proposed exposure fusion algorithm allows fine details to be exaggerated while existing exposure fusion algorithms do not provide such an option. The proposed scheme usually outperforms existing exposure fusion schemes when there are moving objects in real scenes. In addition, the proposed ghost removal algorithm is simpler than existing ghost removal algorithms and is suitable for mobile devices with limited computational resource.

Hardware-Efficient Bilateral Filtering for Stereo Matching

Abstract: This paper presents a new bilateral filtering method specially designed for practical stereo vision systems. Parallel algorithms are preferred in these systems due to the real-time performance requirement. Edge-preserving filters like the bilateral filter have been demonstrated to be very effective for high-quality local stereo matching. A hardware-efficient bilateral filter is thus proposed in this paper. When moved to an NVIDIA GeForce GTX 580 GPU, it can process a one megapixel color image at around 417 frames per second. This filter can be directly used for cost aggregation required in any local stereo matching algorithm. Quantitative evaluation shows that it outperforms all the other local stereo methods both in terms of accuracy and speed on Middlebury benchmark. It ranks 12th out of over 120 methods on Middlebury data sets, and the average runtime (including the matching cost computation, occlusion handling, and post processing) is only 15 milliseconds (67 frames per second).

An FPGA-Based Fully Synchronized Design of a Bilateral Filter for Real-Time Image Denoising

Abstract: In this paper, a detailed description of a synchronous field-programmable gate array implementation of a bilateral filter for image processing is given. The bilateral filter is chosen for one unique reason: It reduces noise while preserving details. The design is described on register-transfer level. The distinctive feature of our design concept consists of changing the clock domain in a manner that kernel-based processing is possible, which means the processing of the entire filter window at one pixel clock cycle. This feature of the kernel-based design is supported by the arrangement of the input data into groups so that the internal clock of the design is a multiple of the pixel clock given by a targeted system. Additionally, by the exploitation of the separability and the symmetry of one filter component, the complexity of the design is widely reduced. Combining these features, the bilateral filter is implemented as a highly parallelized pipeline structure with very economical and effective utilization of dedicated resources. Due to the modularity of the filter design, kernels of different sizes can be implemented with low effort using our design and given instructions for scaling. As the original form of the bilateral filter with no approximations or modifications is implemented, the resulting image quality depends on the chosen filter parameters only. Due to the quantization of the filter coefficients, only negligible quality loss is introduced.

Bilateral Filtering-Based Enhanced Pansharpening of Multispectral Satellite Images

Abstract: An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance.

Directional Joint Bilateral Filter for Depth Images

Abstract: Depth maps taken by the low cost Kinect sensor are often noisy and incomplete. Thus, post-processing for obtaining reliable depth maps is necessary for advanced image and video applications such as object recognition and multi-view rendering. In this paper, we propose adaptive directional filters that fill the holes and suppress the noise in depth maps. Specifically, novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented. Experimental results show that our method yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.

Multispectral Image Denoising With Optimized Vector Bilateral Filter

Abstract: Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios (SNRs). Typical vector bilateral filtering described in the literature does not use parameters satisfying optimality criteria. We introduce an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimization of the Stein's unbiased risk estimate of this nonlinear estimator. Along the way, we provide a plausibility argument through an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared with several other approaches.

A graph-based joint bilateral approach for depth enhancement

Abstract: Depth images are often presented at a lower spatial resolution, either due to limitations in the acquisition of the depth or to increase compression efficiency. As a result, upsampling low-resolution depth images to a higher spatial resolution is typically required prior to depth image based rendering. In this paper, depth enhancement and up-sampling techniques are proposed using a graph-based formulation. In one scheme, the depth is first upsampled using a conventional method, then followed by a graph-based joint bilateral filtering to enhance edges and reduce noise. A second scheme avoids the two-step processing and upsamples the depth directly using the proposed graph-based joint bilateral upsampling. Both filtering and interpolation problems are formulated as regularization problems and the solutions are different from conventional approaches. Further, we also studied operations on different graph structures such as star graph and 8-connected graph. Experimental results show that the proposed methods produce slightly more accurate depth at the full resolution with improved rendering quality of intermediate views.

A General Framework for Bilateral and Mean Shift Filtering.

Abstract: We present a generalization of the bilateral filter that can be applied to feature-preserving smoothing of signals on images, meshes, and other domains within a single unified framework. Our discretization is competitive with state-of-the-art smoothing techniques in terms of both accuracy and speed, is easy to implement, and has parameters that are straightforward to understand. Unlike previous bilateral filters developed for meshes and other irregular domains, our construction reduces exactly to the image bilateral on rectangular domains and comes with a rigorous foundation in both the smooth and discrete settings. These guarantees allow us to construct unconditionally convergent mean-shift schemes that handle a variety of extremely noisy signals. We also apply our framework to geometric edge-preserving effects like feature enhancement and show how it is related to local histogram techniques.

Acceleration of Shiftable O (1) Algorithm for Bilateral Filtering and Non-local means

Abstract: A direct implementation of the bilateral filter requires O(σs 2 ) operations per pixel, where σs is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter was recently proposed that require O(1) operations per pixel with respect to σs. This is done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter is realized by approximating the Gaussian range kernel using raised cosines. Later, it is demonstrated that this idea could be extended to a larger class of filters, including the popular non-local means filter. For an image with dynamic range (0, T), the run time scaled as O(T 2 /σr 2 ) with σr. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. This project discusses this problem and propose some advanced methods to accelerate the implementation, in general, and for small σr in particular and also provides some experimental results to demonstrate the acceleration that is achieved using these modifications.

Real-time stereo using approximated joint bilateral filtering and dynamic programming

Abstract: 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.

VLSI Architecture Design of Guided Filter for 30 Frames/s Full-HD Video

Abstract: Filtering is widely used in image and video processing for various applications Recently, the guided filter has been proposed and became one of the popular filtering methods In this paper, to achieve the computation demand of guided filtering in full-HD video, a double integral image architecture for guided filter ASIC design is proposed In addition, a reformation of the guided filter formula is proposed, which can prevent the error resulted from truncation in the fractional part and modify the regularization parameter e on user's demand The hardware architecture of the guided image filter is then proposed and can be embedded in mobile devices to achieve real-time HD applications To the best of our knowledge, this paper is also the first ASIC design for guided image filter With a TSMC 90-nm cell library, the design can operate at 100 MHz and support for Full-HD (1920 × 1080) 30 frame/s with 929K gate counts and 32 KB on-chip memory Moreover, for the hardware efficiency, our architecture is also the best compared to other previous works with bilateral filter

Gaussian and Gabor Filter Approach for Object Segmentation

Abstract: The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA) for object segmentation. An improved and efficient approach based on Gaussian and Gabor Filter reads the given input image and performs filtering and smoothing operation. The region occupied by the object is extracted from the image by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing. The proposed approach experimented on standard images taken from Caltech datasets, Corel Photo CDs, and Weizmann horse datasets show significantly improved results.

Efficient adaptive fuzzy-based switching weighted average filter for the restoration of impulse corrupted digital images

Abstract: This study proposes a new fuzzy adaptive filter for the restoration of impulse corrupted digital images. The proposed filter incorporates fuzzy functions to model the uncertainties, while detecting and correcting impulses. The traditional, SMALL fuzzy function is used to identify the non-impulsive nature of the detected corrupted pixels in the initial step. For the better restoration of detected impulsive pixels, a modified version of Gaussian function is utilised to determine the similarity among the detected uncorrupted pixels. The proposed correction scheme provides more weight to the uncorrupted pixels that show much similarity with other uncorrupted pixels in the window while replacing impulses. The proposed filter adapts to various noisy and image conditions and is capable of suppressing noise while preserving image details. The experimental results in terms of subjective and objective metrics favour the proposed algorithm than many other prominent filters in literature.

Ultrasound image denoising using a combination of bilateral filtering and stationary wavelet transform

Abstract: Medical image degradation has a significant impact on image quality, and thus affects human interpretation and the accuracy of computer-assisted diagnostic techniques. Unfortunately, Ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, despekle filtering is a critical preprocessing step in medical ultrasound images. In this paper we propose a new image denoising technique based on the combination of bilateral filter and stationary wavelet transform. The main contribution of this paper is in the use of a new neighborhood relationship to develop a new multiscale bilateral filter. Experimental results validated the effectiveness and the accuracy of the proposed filter in speckle noise reduction and edge preservation for medical ultrasound images.

Dynamic Positron Emission Tomography Image Restoration via a Kinetics-Induced Bilateral Filter

Abstract: Dynamic positron emission tomography (PET) imaging is a powerful tool that provides useful quantitative information on physiological and biochemical processes. However, low signal-to-noise ratio in short dynamic frames makes accurate kinetic parameter estimation from noisy voxel-wise time activity curves (TAC) a challenging task. To address this problem, several spatial filters have been investigated to reduce the noise of each frame with noticeable gains. These filters include the Gaussian filter, bilateral filter, and wavelet-based filter. These filters usually consider only the local properties of each frame without exploring potential kinetic information from entire frames. Thus, in this work, to improve PET parametric imaging accuracy, we present a kinetics-induced bilateral filter (KIBF) to reduce the noise of dynamic image frames by incorporating the similarity between the voxel-wise TACs using the framework of bilateral filter. The aim of the proposed KIBF algorithm is to reduce the noise in homogeneous areas while preserving the distinct kinetics of regions of interest. Experimental results on digital brain phantom and in vivo rat study with typical (18)F-FDG kinetics have shown that the present KIBF algorithm can achieve notable gains over other existing algorithms in terms of quantitative accuracy measures and visual inspection.