Denoising efficiency for multichannel images corrupted by signal-dependent noise

Essential improvements in quality of original images formed by multichannel (multi- and hyperspectral) sensors have been gained in recent years. In particular, level of thermal noise in acquired images has been sufficiently reduced [1]. However, there are still component (sub-band) images in obtained data for which noise level is quite high [2, 3]. One more peculiarity is that signal-dependent noise component is characterized by dominant contribution [3] for new generation of sensors. Sometimes, the component images with the lowest signal-to-noise ratio (SNR) are ignored at stages of multichannel image classification and interpreting [1, 2]. However, recent studies have demonstrated that useful information can be extracted from “noisy” sub-band images under condition that noise is reduced by an efficient pre-filtering technique [2]. Thus, an actual task is to design such efficient techniques able to cope with signal-dependent noise and to analyze their performance.