Efficient Super-Resolution driven by saliency selectivity

This paper presents a low-complexity saliency detector targeted towards efficient selective Super-Resolution (SR). As a result, an improved efficient ATtentive-SELective Perceptual (AT-SELP) framework is presented. The proposed AT-SELP scheme results in a reduced computational complexity for iterative SR algorithms without any perceptible loss in the desired enhanced image/video quality. A perceptually significant set of active pixels is selected for processing by the SR algorithm based on a local contrast sensitivity threshold model and the proposed low complexity saliency detector. Simulation results show that the proposed AT-SELP scheme results in a 15-40% reduction in computational complexity over an efficient Selective Perceptual (SELP) SR scheme without degradation in the visual quality.