Artifact reduction for separable non-local means

It was recently demonstrated [J. Electron. Imaging, 25(2), 2016] that one can perform fast non-local means (NLM) denoising of one-dimensional signals using a method called lifting. The cost of lifting is independent of the patch length, which dramatically reduces the run-time for large patches. Unfortunately, it is difficult to directly extend lifting for non-local means denoising of images. To bypass this, the authors proposed a separable approximation in which the image rows and columns are filtered using lifting. The overall algorithm is significantly faster than NLM, and the results are comparable in terms of PSNR. However, the separable processing often produces vertical and horizontal stripes in the image. This problem was previously addressed by using a bilateral filter-based post-smoothing, which was effective in removing some of the stripes. In this letter, we demonstrate that stripes can be mitigated in the first place simply by involving the neighboring rows (or columns) in the filtering. In other words, we use a two-dimensional search (similar to NLM), while still using one-dimensional patches (as in the previous proposal). The novelty is in the observation that one can use lifting for performing two-dimensional searches. The proposed approach produces artifact-free images, whose quality and PSNR are comparable to NLM, while being significantly faster.Comment: To appear in Journal of Electronic Imagin

Fast Separable Non-Local Means

We propose a simple and fast algorithm called PatchLift for computing distances between patches (contiguous block of samples) extracted from a given one-dimensional signal. PatchLift is based on the observation that the patch distances can be efficiently computed from a matrix that is derived from the one-dimensional signal using lifting; importantly, the number of operations required to compute the patch distances using this approach does not scale with the patch length. We next demonstrate how PatchLift can be used for patch-based denoising of images corrupted with Gaussian noise. In particular, we propose a separable formulation of the classical Non-Local Means (NLM) algorithm that can be implemented using PatchLift. We demonstrate that the PatchLift-based implementation of separable NLM is few orders faster than standard NLM, and is competitive with existing fast implementations of NLM. Moreover, its denoising performance is shown to be consistently superior to that of NLM and some of its variants, both in terms of PSNR/SSIM and visual quality

Fast O(1) bilateral filtering using trigonometric range kernels

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.Comment: Accepted in IEEE Transactions on Image Processing. Also see addendum: https://sites.google.com/site/kunalspage/home/Addendum.pd

AN EFFECTIVE APPROACH OF BILATERAL FILTER IMPLEMENTATION IN SPARTAN-3 FIELD PROGRAMMABLE GATE ARRAY

This paper presents the Field Programmable Gate Array (FPGA) implementation of Bilateral Filter, in order to achieve high performance and low power consumption. Bilateral filtering is a technique to smooth images while preserving edges by means of a nonlinear combination of nearby image values. This method is nonlinear, local, and simple. We give an idea that bilateral filtering can be accelerated by bilateral grid scheme that enables fast edge-aware image processing. Nowadays, most of the applications require real time hardware systems with large computing potentiality for which fast and dedicated Very Large Scale Integration (VLSI) architecture appears to be the best possible solution. While it ensures high resource utilization, that too in cost effective platforms like FPGA, designing such architecture does offers some flexibilities like speeding up the computation by adapting more pipelined structures and parallel processing possibilities of reduced memory consumptions. Here we have developed an effective approach of bilateral filter implementation in Spartan-3 FPGA

A New Real-Time Embedded Video Denoising Algorithm

International audienceMany embedded applications rely on video processing or on video visualization. Noisy video is thus a major issue for such applications. However, video denoising requires a lot of computational effort and most of the state-of-the-art algorithms cannot be run in real-time at camera framerate. This article introduces a new real-time video denoising algorithm for embedded platforms called RTE-VD. We first compare its denoising capabilities with other online and offline algorithms. We show that RTE-VD can achieve real-time performance (25 frames per second) for qHD video (960×540 pixels) on embedded CPUs and the output image quality is comparable to state-of-the-art algorithms. In order to reach real-time denoising, we applied several high-level transforms and optimizations (SIMDization, multi-core parallelization, operator fusion and pipelining). We study the relation between computation time and power consumption on several embedded CPUs and show that it is possible to determine different frequency and core configurations in order to minimize either the computation time or the energy

Real-time embedded video denoiser prototype

International audienceLow light or other poor visibility conditions often generate noise on any vision system. However, video denoising requires a lot of computational effort and most of the state-of-the-art algorithms cannot be run in real-time at camera framerate. Noisy video is thus a major issue especially for embedded systems that provide low computational power. This article presents a new real-time video denoising algorithm for embedded platforms called RTE-VD [1]. We first compare its denoising capabilities with other online and offline algorithms. We show that RTE-VD can achieve real-time performance (25 frames per second) for qHD video (960x540 pixels) on embedded CPUs with an output image quality comparable to state-of-the-art algorithms. In order to reach real-time denoising, we applied several high-level transforms and optimizations. We study the relation between computation time and power consumption on several embedded CPUs and show that it is possible to determine find out frequency and core configurations in order to minimize either the computation time or the energy. Finally, we introduce VIRTANS our embedded real-time video denoiser based on RTE-VD