一种自适应正则化的图像超分辨率算法

提出一种自适应正则化的图像超分辨率重建算法. 首先, 利用局部残差均值自适应地计算各低分辨率图像通道的权值参数矩阵, 可有效地利用各通道对应区域间的交叉信息; 其次, 利用正则项局部误差均值自适应地计算平衡正则项和保真项的正则化参数矩阵, 能较好地保持图像边缘纹理等信息.实验结果表明本文算法不但具有较高峰值信噪比(Peak signal to noise ratio, PSNR) 和结构相似度(Structural similarity, SSIM), 而且在边缘、纹理等细节区域具有更好的重建效果.

An Adaptive-regularized Image Super-resolution

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.