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Modeling of Surveillance Video Noise
Signal Processing, Pattern Recognition, and Applications / 722: Computer Graphics and Imaging, 2011This paper aims at breaking new ground in modeling and estimation of recording noise of surveillance video for further development of new techniques to restore video images. In order to tackle the video denoising problem with non-stationary image contents and various noise sources, a critical task is estimation of varieties of noise in video signals ...
Zhengya Xu +3 more
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Mutual Noise Estimation Algorithm for Video Denoising
2019 IEEE International Conference on Image Processing (ICIP), 2019This paper presents a novel algorithm to estimate spatio-temporal noise variance in videos. The algorithm uses mutual information from the spatial and temporal noise statistics to detect homogeneous blocks in a video frame and estimate the noise. The experimental results show the accuracy and robustness of the algorithm over videos with low to high ...
Mohammad Izadi +2 more
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Video oriented filter for impulse noise reduction
Journal of Visual Communication and Image Representation, 2018Abstract A window-adaptive video filter for removal of impulse noise from grayscale videos is proposed. The new method is based on local orientation estimation. The dominant orientation of the pattern in a local spatial neighborhood is computed by minimizing an expression of directional derivatives, and at the same time the orientation strength is ...
Lianghai Jin +3 more
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A Note on Video Noise Weighting
IEEE Transactions on Broadcasting, 1964A dicussion on video noise weighting is presented. A standard way of comparing results is proposed.
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Measuring noise correlation for improved video denoising
2012 19th IEEE International Conference on Image Processing, 2012The vast majority of previous work in noise reduction for visual media has assumed uncorrelated, white, noise sources. In practice this is almost always violated by real media. Film grain noise is never white, and this paper highlights that the same applies to almost all consumer video content.
Anil C. Kokaram +3 more
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Video background modeling under impulse noise
2014 IEEE International Conference on Image Processing (ICIP), 2014Video background modeling is an important task in many video processing applications. Most existing algorithms assume a Gaussian noise model, but digital videos are, in practice, prone to be degraded by impulse noise, due to transmission errors in wireless or high data-rate wired channels.
Paul Rodríguez 0001, Brendt Wohlberg
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Impact of Noise on Digital Video Traffic Characteristics
2009 Third International Conference on Multimedia and Ubiquitous Engineering, 2009In this paper, we investigated the impacts of temporal noise and spatial noise on video traffic characteristics. Our study demonstrated that the noise of camera seriously changes the features of motion JPEG sequences and MPEG sequences. The results of experiment can be used for traffic modeling.
Xiaohui Chen, Weidong Wang, Guo Wei 0001
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Temporal noise reduction of Bayer matrixed video data
Proceedings. IEEE International Conference on Multimedia and Expo, 2003This paper describes a new approach for noise reduction of video sequences that directly processes raw data frames acquired by an image sensor. The proposed noise reduction filter operates on Bayer matrixed video sequences instead of the canonical YUV format allowing saving of resources in terms of time and space; this is particularly relevant for real
A. BOSCO +3 more
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Robust Fuzzy Filter for Noise Reduction in Video Deinterlacing
2008 IEEE International Conference on Signal Image Technology and Internet Based Systems, 2008This paper describes the design and evaluation of a robust noise reduction fuzzy filter, and considers its application to video deinterlacing. The proposed fuzzy filter is designed to reduce mixed noise (random and impulse) and to be used in real-time deinterlacing.
Gwanggil Jeon +5 more
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Alpha-Stable Noise Reduction in Video Sequences
2004In this paper, a nonlinear motion-compensated filter is described for removing α-stable noise from video sequences. To address this problem, we propose a spatio-temporal adaptive weighted myriad and l p -norm filters. Prior to filtering, motion compensation is performed by using a block-matching algorithm with p-norm matching error function.
Mohammed El Hassouni, Hocine Cherifi
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