Cloud covering denoising through image fusion

Abstract

This paper presents a solution to the cloud removal problem, based in a recently developed image fusion methodology consisting in applying a 1-D pseudo-Wigner distribution (PWD) transformation to the source images and on the use of a pixel-wise cloud model. Both features could also be interpreted as a denoising method centered in a pixel-level measure. Such procedure is able to process sequences of multi-temporal registered images affected with spatial-variant noise. The goal consists in providing a 2-D clean image, after removing the spatial-variant noise disturbing the set of multi-temporal registered source images. This is achieved by taking as reference a statistically parameterized model of a cloud prototype. Using this model, a pixel-wise measure of the noise degree of the source images can be calculated through their PWDs. This denoising procedure enables to choose the noise-free pixels from the set of given source images. The applicability of the method to the cloud removal paradigm is illustrated with different sets of artificial and natural cloudy or foggy images, partially occluded by clouds in different regions. Another advantage of the present approach is its reduced computational cost, once the 1-D case has been preferred instead of a full 2-D implementation of the PWD.