For deringing, each 8×8 block in a field is first classified into one of the four categories, i.e., strong edge, weak edge, texture and smooth blocks. For deblocking, a 1D fuzzy filter with different window sizes is used to remove the horizontal and vertical blocking artifacts respectively. The method takes the interlaced video format into consideration and processes each field separately. The paper presents a new method using fuzzy filtering to remove the coding artifacts in compressed video. The experimental results show that at reasonable computational cost it achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality. We develop an efficient realization of this video denoising algorithm. Significant improvement of this approach is achieved by using a two-step algorithm where an intermediate estimate is produced by grouping and collaborative hard-thresholding and then used both for improving the grouping and for applying collaborative empirical Wiener filtering. Since, in general, the obtained block estimates are mutually overlapping, we aggregate them by a weighted average in order to form a non-redundant estimate of the video. This filtering - that we term "collaborative filtering"- exploits the correlation between grouped blocks and the corresponding highly sparse representation of the true signal in the transform domain. Each formed 3D group is filtered by a 3D transform-domain shrinkage (hard-thresholding and Wiener filtering), the result of which are estimates of all grouped blocks. This grouping is realized as a spatio-temporal predictive-search block-matching, similar to techniques used for motion estimation. A noisy video is processed in blockwise manner and for each processed block we form a 3D data array that we call "group" by stacking together blocks found similar to the currently processed one. We propose an effective video denoising method based on highly sparse signal representation in local 3D transform domain. We show that while the Multi-Layer Perceptron and Bilevel MRF algorithms work as well as or even better than BM3D on synthetic noise, they lag behind on our dataset. Finally, we exemplify the use of our dataset by evaluating four denoising algorithms: Active Random Field, BM3D, Bilevel MRF optimization, and Multi-Layer Perceptron. We also introduce a method for estimating the true noise level in each of our images, since even the low noise images contain a small amount of noise. The dataset contains over 120 scenes and more than 400 images, including both 16-bit RAW images and 8-bit BMP pixel and intensity-aligned images from 2 digital cameras (Canon S90 and Canon T3i) and a mobile phone (Xiaomi Mi3). In this paper we introduce a benchmark dataset of uncompressed color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. These trained algorithms and their evaluations on synthetic data may lead to incorrect conclusions about their performances on real noise. This repository collects the state-of-the-art algorithms for video/image enhancement using deep learning (AI) in recent years, including reviews and engineering practices.Many modern and popular state of the art image denoising algorithms are trained and evaluated using images corrupted by artificial noise.
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