Enhancing Video Denoising Algorithms by Fusion from Multiple Views

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.