Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Nonlocal Image and Movie Denoising
International Journal of Computer Vision
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
Image sequence denoising via sparse and redundant representations
IEEE Transactions on Image Processing
Recursive displacement estimation and restoration of noisy-blurred image sequences
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Spatio-temporal adaptive 3-D Kalman filter for video
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
The SURE-LET Approach to Image Denoising
IEEE Transactions on Image Processing
Wavelet-Domain Video Denoising Based on Reliability Measures
IEEE Transactions on Circuits and Systems for Video Technology
SURE-LET for Orthonormal Wavelet-Domain Video Denoising
IEEE Transactions on Circuits and Systems for Video Technology
Video Denoising Based on a Spatiotemporal Gaussian Scale Mixture Model
IEEE Transactions on Circuits and Systems for Video Technology
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Video denoising is highly desirable in many real world applications. It can enhance the perceived quality of video signals, and can also help improve the performance of subsequent processes such as compression, segmentation, and object recognition. In this paper, we propose a method to enhance existing video denoising algorithms by denoising a video signal from multiple views (front-, top-, and side-views). A fusion scheme is then proposed to optimally combine the denoised videos from multiple views into one. We show that such a conceptually simple and easy-to-use strategy, which we call multiple view fusion (MVF), leads to a computationally efficient algorithm that can significantly improve video denoising results upon state-of-the-art algorithms. The effect is especially strong at high noise levels, where the gain over the best video denoising results reported in the literature, can be as high as 2-3 dB in PSNR. Significant visual quality enhancement is also observed and evidenced by improvement in terms of SSIM evaluations.