LEAST-SQUARES RESTORATION OF MULTICHANNEL IMAGES

In this paper we consider the problem of multichannel restoration using both within- and between-channel deterministic information. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images will yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using two approaches, a set theoretic and a constrained optimization. A geometric interpretation of the estimates of both filters is given. Color images, that is, three-channel imagery with red, green, and blue components, are considered. Constraints that capture the within-and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Finally, experiments using color images are shown.