Spatial domain denoising is usually done with weighted averaging within local 2-D or 3-D windows, where the weights can be either fixed or adapted based on the local image content. Video denoising algorithms may be roughly classified based on two different criteria: whether they are implemented in the spatial domain or transform domain and whether motion information is directly incorporated. Removing/reducing noise in video signals (or video denoising) is highly desirable, as it can enhance perceived image quality, increase compression effectiveness, facilitate transmission bandwidth reduction, and improve the accuracy of the possible subsequent processes such as feature extraction, object detection, motion tracking and pattern classification. Video signals are often contaminated by noise during acquisition and transmission. Keywords polyview fusion, video denoising, video quality enhancement, PSNR. Where the improvement over state-of-the-art denoising algorithms is often more than 2 dB in PSNR. And the extensive tests using a variety of base video-denoising algorithms show that the proposed method leads to surprisingly significant and consistent gain in terms of PSNR. A fusion algorithm is then designed to merge the resulting multiple denoised videos into one, so that the visual quality of the fused video is improved. The idea is to denoise the noisy video as a 3-D volume using a given base 2-D denoising algorithm but applied from multiple views (front, top, and side views). Here the proposed algorithm is a effective strategy that aims to enhance the performance of existing video denoising algorithms.
It can enhance the perceived quality of video signals, and can also help improve the performance of subsequent processes such as com-press ion, segmentation, and object recognition. M.TECH, ISE, EWIT, Bangalore, Karnataka 2Ībstract video denoising is highly desirable in many real world applications. An Approach And Design To Enhance Video Denoising Algorithms
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