基于稀疏表示与矩阵填充的多帧超分辨率图像重建

针对现有算法在通用图像分辨率要求较高时重建效果不稳定的问题,提出一种基于稀疏表示与矩阵填充的多帧超分辨率图像重建算法.对自然图像库进行训练建立过完备词典对,并将低分辨率图像分成若干图像块,根据局部先验约束建立稀疏表示模型,通过线性规划的方法求得过完备词典对下图像块的稀疏系数;利用多帧图像序列的相似性,采用非精确增广拉格朗日乘子法对全局约束构建的观测矩阵进行矩阵填充和矩阵恢复,最终获得高分辨率图像.实验结果表明,与其他主流算法相比,重建后的图像保留了更丰富的图像边缘与细节信息,不会过于平滑而导致图像模糊,并且不易受过完备词典选择范围的影响,具有较好的稳定性和更高的峰值信噪比,可应用于遥感图像超分辨率重建等图像应用领域.

Multi-frame Image Super Resolution Based on Sparse Representation and Matrix Completion

To solve the unstable generic image reconstruction problem,an improved image super resolution algorithm was presented for exploiting the properties of sparse representation and matrix completion.Over-complete dictionary pair was established by training natural images.According to the local prior constraints,sparse coefficients of each low resolution image patches would be estimated.In multi-frame images,the sparse coefficients of each frame were similar.Therefore,the inexact augmented Lagrange multiplier method was adopted to achieve matrix completion and recovery in the process of recurring global constraints.The final high resolution image would be generated from output low-rank matrix.Resultsshow that the methods are effective in retaining the image edges and details,and it is of robustness to the scope of dictionary with higher PSNR value.This algorithm can be applied in remote sensing image super-resolution reconstruction areas.