Color video denoising using epitome and sparse coding

• A good denoising method is vital as it able to enhance the performance of next processes.
• This paper extends the Benoit et al. work from monocular image to color video domain.
• VESC (Video Epitome & Sparse Coding) framework is proposed for the video denoising task.
• We show comparable results to conventional methods in both spatial and transform domain.
• We also demonstrate the strength of the proposed method in visual tracking problem.

Denoising is a process that remove noise from a signal. In this paper, we present a unified framework to deal with video denoising problems by adopting a two-steps process, namely the video epitome and sparse coding. First, the video epitome will summarize the video contents and remove the redundancy information to generate a single compact representation to describe the video content. Second, employing the single compact representation as an input, the sparse coding will generate a visual dictionary for the video sequence by estimating the most representative basis elements. The fusion of these two methods have resulted an enhanced, compact representation for the denoising task. Experiments on the publicly available datasets have shown the effectiveness of our proposed system in comparison to the state-of-the-art algorithms in the video denoising task.