Image denoising using sparse approximation with adaptive window selection

In this paper the problem of image denoising is approached using sparse approximation of local image patches. The small patches are extracted by sliding square windows. An adaptive window selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive window selection yields the minimum mean square error (MMSE) in recovered image. This framework gives us a clustered image based on the selected window size, then each cluster is denoised separately using sparse approximation. The results obtained using the proposed framework are very much comparable with the recently proposed denoising techniques.