Fast single frame super-resolution using perceptual visibility optimization
Luhong Liang, Peng Luo, Wai Keung Cheung, King Hung Chiu · 2014
Example-based super-resolution (SR) approaches mostly reconstruct and optimize the high-resolution (HR) image according to objective criteria such as imaging model. However, in consumer electronics applications the ultimate goal is better subjective visual effect rather than higher objective texture visibility. In this paper, we propose a SR method using perceptual visibility optimization (PVO). A human visual system (HVS) preference model is built based on just noticeable distortion (JND) threshold that considers the structural regularity of the texture. Following the scale-invariant self-similarity (SiSS) based SR reconstruction, an iterative backprojection (IBP) procedure coupled with the proposed model adaptively enhances the texture visibility to match the HVS's preference. Experimental results show the proposed approach has competitive quality and lower computational complexity compared with several state-of-the-art SR approaches.