Image restoration consists in retrieving an original image by processing captured images of a scene which are degraded by noise, blurring or optical scattering. Commonly restoration algorithms utilize a single monocular image of the observed scene by assuming a known degradation model. In this approach, valuable information of the three dimensional scene is discarded. This work presents a locally-adaptive algorithm for image restoration by employing stereo vision. The proposed algorithm utilizes information of a three-dimensional scene as well as local image statistics to improve the quality of a single restored image by processing pairs of stereo images. Computer simulations results obtained with the proposed algorithm are analyzed and discussed in terms of objective metrics by processing stereo images degraded by optical scattering.
In image restoration problems it is commonly assumed that image degradations are linear. In real-life this assumption is not always satisfied causing linear restoration methods fail. In this work, we present the design of an image restoration filtering based on genetic programming. The proposed filtering is given by a secuence of basic mathematical operators that allows to retrieve an undegraded image from an image degraded with noise. Computer simulations results obtained with the proposed algorithm in terms of objective metrics are analyzed and discussed by processing images degraded with noise. The obtained results are compared with those obtained with existing linear filters.