Positive incentive CNN structure coupled nonconvex model for image super-resolution

Abstract

This paper studies super-resolution (SR) technique to reconstruct high-quality images for deep image analysis. Currently, the convolutional neural networks (CNNs) are well performing methods and the finding that random noise added in the network can have positive incentive effect, we innovatively propose a positive incentive CNNs. However, concerning the uncontrollable characteristic and lack consistency of deep network, we propose a novel framework that joins nonconvex model based on framelet and positive incentive CNN structure, which can impose consistency between the high-resolved image and the given low-resolution image, and depict image information by sparse representation. Furthermore, to overcome the challenge of computing the minimizer of the nonconvex problem, we use proximal linearized minimization (PLM) algorithm to convex the nonconvex term, then apply the alternating direction method of multipliers (ADMM) as the solver which can converge to a stationary point of the nonconvex model. The experimental outcomes on Set5, Set14, BSD100, Urban100, and real-world images demonstrate that the proposed approach outperforms the state-of-the-art methods in terms of peak signal to noise ratio (PSNR) value, structural similarity index (SSIM), and visual quality.