Content adaptive video denoising based on human visual perception |
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| Affiliation: | 1. State Key Lab of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China;2. Zhejiang Wanli University, Ningbo, China;3. Institute of Software, Chinese Academy of Sciences, Beijing, China;4. University of Thessaly, Volos, Greece;1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China;2. College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;1. School of Computer Science, Fudan University, Shanghai 201203, China;2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;2. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China;3. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;4. Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China |
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| Abstract: | In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures. |
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| Keywords: | Content adaptive Adaptive noise filtering Human visual perception Noisy frame detection Motion detection Nonlocal mean filtering Perceptual video denoising Temporal similarity |
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