A phase congruency based patch evaluator for complexity reduction in multi-dictionary based single-image super-resolution

Single-image based super-resolution (SISR) aims to recover a high-resolution (HR) image from one of its degraded low-resolution (LR) images. To improve the quality of reconstructed HR image, many researchers attempt to adopt multiple pairs of dictionaries to sparsely represent the image patches. Conventionally, all the patches with different contents are treated equally, and each patch is coded by multiple pairs of dictionaries, which results in tremendous computational burden in the reconstruction process. In this paper, a phase congruency (PC) based patch evaluator (PCPE) is proposed to divide the LR patches into three categories: significant, less-significant and smooth based on the complexity of the contents. Thus, a flexible multi-dictionary based SISR (MDSISR) framework is proposed, which reconstructs different patches by different approaches. In this framework, multiple dictionaries are only applied to scale up the significant patches to maintain high reconstruction accuracy. Also, two simpler baseline approaches are used to reconstruct the less-significant and smooth patches, respectively. Experimental studies on benchmark database demonstrate that the proposed method can achieve competitive PSNR, SSIM, and FSIM with some state-of-the-art SISR approaches. Besides, it can reduce the computational cost in conventional MDSISR significantly without much degradation in visual and numerical results.