Selective Multi-Source Total Variation Image Restoration

This paper is concerned with automatically fusing multiple noisy and partially corrupted source images into a single denoised image. To create the fused image we minimise a convex objective function, which ensures spatial smoothness through total variation regularisation, and similarity to the source images via pixel-wise selective regularisation against each of the source images. We call this approach Selective Multi-Source Total Variation Image Restoration (SMTV). Applications of SMTV include noise removal in low-light conditions, enhancement of images from low quality or damaged imaging sensors and haze or cloud removal from satellite imagery. Experimental evaluation demonstrates that the fusion of multiple images results in a more accurate recovery than single image restoration.