Compressive Sensing recovery with improved hybrid filter

Compressive Sensing (CS) is a novel sampling framework which is more efficient than the Nyquist sampling for sparse signals. A major challenge in CS is its quality improvement of recovered signal when noise exists. To reduce noise in the recovered images, filters are usually employed. This paper focuses on improving the quality of CS recoveries by applying a hybrid filter which pursues smoothness and preserves edge at the same time. Considering desirability of the block-based recovery in practical usages, the proposed hybrid filter is investigated not only for the frame-based recovery but also for the block-based recovery. Experimental results demonstrate that the proposed hybrid filter attains much better performance in CS recovery than the conventional ones in term of both subjective and objective qualities.