改进的POCS算法的超分辨率单幅图像重建

An Improved POCS Algorithm for Super-resolution Single Image Reconstruction

超分辨率图像重建的目的是从几个低分辨率(LR)的模糊、有噪声、欠采样以及移位的图像中获得一个高分辨率(HR)高质量的图像。论文基于凸集投影的超分辨率图像重建算法,分析重建后图像边缘模糊的成因,提出了一种保留边缘的超分辨率变正则化图像重建的方法。该方法对退化图像建立精确的退化过程模型,通过自适应选取正则化参数动态调节重建误差逼近项和凸集约束项,从而实现超分辨率重建。在几张图片上的测试效果证明了论文算法相对于现有其他方法的优越性,该算法能够有效地保证复原求解的收敛性并保持复原图像的边缘细节,且峰值信噪比具有较大提升,对经模糊和噪声污染的图像有较好的复原效果。

Super-resolution image reconstruction has been one of the most important research areas in recent years,whose goals is are to obtain a high resolution(HR)image from several low resolution(LR)blurred,noisy,under sampled and displaced images.Relation of the HR image and LR images can be modeled by a linear system using a transformation matrix and additive noise.However,an unique solution may not be available because of the singularity of transformation matrix.To overcome thisill-posed problem,stochastic methods such as ML and MAP have been introduced.However,their performance is not good because the effect of noise energy has been ignored.In this paper,an adaptive regularization approach is proposed based on the fact that the regularization parameter should be a linear function of noise variance.The performance of the proposed approach has been tested on several images and the obtained results demonstrate the superiority of our approach compared with existing methods.