一种遥感影像超分辨率重建的稀疏表示建模及算法

A Super-resolution Model and Algorithm of Remote Sensing Image Based on Sparse Representation

为了对单幅低分辨率遥感影像的空间分辨率进行增强,提出一种基于稀疏表示的超分辨率重建方法。该方法首先采用优化最小化方法学习高-低分辨率联合字典对,通过构造一个参数互相解耦的易于优化的代理函数,替代原来的参数互相耦合难以优化的目标函数,保证每一次迭代求解的值在局部范围内最优。然后,将学习的字典对用以指导其他低分辨率遥感影像的超分辨率重建。试验表明,与传统的插值方法相比,本研究算法在客观的评价指标上具有一定的提高,在主观的视觉效果上也取得一些改善,可为其他单幅低分辨率遥感影像的超分辨率重建提供有用的高频细节信息,具有一定的普适性。

In order to enhance the spatial resolution of a single remote sensing image,a super-resolution reconstruction method based on sparse representation is presented in this work.First,apair of dictionaries for low-and high-resolution image patches are learned using the majorization minimization method.The method substitutes the original objective function with a surrogate function that is updated in each optimization step,and can guarantee to find local minima in each optimization step.Second,given a low-resolution remote sensing image,the high-resolution image is reconstructed based on the pair of dictionaries.The experiments show that the state-of-the-art results have been achieved compared to conventional interpolation methods in terms of both PSNR,SSIM and visual perception. The results demonstrate that the algorithm proposed in this paper can provide useful high-frequency details for a single low-resolution remote sensing image in super-resolution reconstruction,and therefore the proposed method is universal.