DENOISING OF MULTICHANNEL IMAGES WITH NONLINEAR TRANSFORMATION OF REFERENCE IMAGE
Sergey Abramov, Victoriya Abramova, Владимир Васильевич Лукин, Karen Egiazarian · Telecommunications and Radio Engineering · 2018
It has been demonstrated recently that efficiency of filtering a noisy component image of a multichannel image can be sufficiently improved under condition that the multichannel image has almost noise-free component image(s) that possess high correlated with the noisy component image used as reference. High correlation and practical absence of the noise are only pre-requisites for efficient filtering of the noisy image using reference. Other criteria of similarity than cross-correlation factor are important. In this paper we show how it is possible to make the reference image very "close" to the noisy one by exploiting nonlinear transformation. Moreover, it is demonstrated that the proposed approach can be useful for denoising images corrupted by signal-dependent noise which is often the case for multichannel remote sensing data.