An efficient approach for image sequence denoising using Zernike moments-based nonlocal means approach

Nonlocal means (NLM)-based denoising is an efficient and simple method for image sequence denoising which has been applied successfully in many image sequence denoising applications. We extend the method for image sequence denoising using Zernike moments (ZMs) called NLM-ZMs which provides better denoising performance as compared to NLM-based approach. In addition, the proposed method is faster because the number of computations needed for block matching and weight computation are significantly reduced. The NLM approach uses the photometric distance between two image patches for determining the similarity distance. In the proposed approach, low order ZMs are used for the block matching process, resulting in better denoising performance at a much lower computation cost. Detailed experimental results are provided to demonstrate better performance and higher speed of the proposed approach as compared to the NLM approach. The results are also compared with the state-of-the-art NLM-based image sequence denoising methods and the denoising results are observed to be competitive with higher speed.