A New Approach For Image Denoising By Motion Estimation And Patch-Based Method

Abstract

Techniques for noise removal in digital images comprise transform thresholding, local averaging, patch based methods and variational techniques. Image fusion is not directly of interest in the removal of noise but in a more general restoration of the image, that is, deblurring, increase of detail or even of resolution. The key of these approaches is the use of a global registration, more robust to noise, blur and color or compression artifacts and, additionally, providing subpixel accuracy. These global registration techniques usually rely on feature matching, for example SIFT, and on a parametric registration, either using an affinity or an homography. The viewfinder alignment performs such a registration by an affine function, with the important characteristic of being extremely fast. A novel image sequence denoising algorithm is presented in the proposed approach takes advantage of the self similarity and redundancy of adjacent frames. The algorithm is inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal average.