CAJ | 학술논문

提出一种基于图像高频和中频信息的超分辨率算法。分别将图像高频和中频作为高分辨图像和低分辨率图像的特征，以图像高频和中频信息作为训练样本对，采用全局迭代收缩方法（ GISA）进行稀疏分解，获得高、中分辨率字典对。根据测试图像对应的中频信息和字典对获得图像高频信息，结合测试图像插值放大结果，经非局部相似性方法处理后获得高分辨率图像。实验结果表明，提出的方法具有较高的重建质量。
제출일충기우도상고빈화중빈신식적초분변솔산법。분별장도상고빈화중빈작위고분변도상화저분변솔도상적특정，이도상고빈화중빈신식작위훈련양본대，채용전국질대수축방법（ GISA）진행희소분해，획득고、중분변솔자전대。근거측시도상대응적중빈신식화자전대획득도상고빈신식，결합측시도상삽치방대결과，경비국부상사성방법처리후획득고분변솔도상。실험결과표명，제출적방법구유교고적중건질량。
An image super-resolution ( SR ) reconstruction algorithm based on high-and mid-frequency components is proposed in this paper .The algorithm selects high-and mid-frequency of natural images as the feature of high-resolution( HR) images and low-resolution( LR) images respectively .Patch pairs, composed of high-and mid-frequency components, are trained by GISA(generalized iterated shrinkage algorithm) to obtain the high-resolution and mid-resolution joint dictionary pair .According to the mid-frequency of the test image and the dictionary pair , image high-frequency is reconstructed .Then, combined with interpolated LR images and reconstructed high-frequency , the HR image is reconstructed after a non-local similarity regularization term.Experimental results show that the proposed method has better performance .