An Adaptive-regularized Image Super-resolution
一种自适应正则化的图像超分辨率算法

This paper presents an adaptive-regularized super-resolution method for image sequence. Firstly, an adaptive weight parameter matrix calculated by local residual mean is used to weight each low-resolution channel, which can utilize the information between channels sufficiently. Secondly, a new adaptive regularization parameter matrix calculated by the neighborhood mean of prior term is determined to balance prior term and fidelity term at each iteration, which can preserve edge and texture well. Experimental results indicate that the proposed method is of higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) and has better reconstruction effect in edge and texture part.