基于模拟退火算法优化的超分辨率图像重建

Super Resolution Image Reconstruction Based on Simulated Annealing Algorithm

超分辨率图像重建技术当前面临诸多挑战,为了重建出高质量的图像,本文提出一种基于模拟退火算法优化的超分辨率图像重建方法。首先将图像划分为多个子块,提取归一化亮度和小波变换子带系数,然后采用流形学习算法对图像特征向量进行提取和降维处理,并采用模拟退火算法优化参数K值,最后将高分辨率图像的梯度作为目标梯度域,通过权值矩阵进行超分辨率图像重建,结果表明,本文方法获得了理想的超分辨率图像重建效果,重建效率高,而且视觉效果、客观评价值均要优于其它超分辨率图像重建方法。

Image super-resolution reconstruction technology currently faces with many challenges,in order to reconstruct a high quality image,this paper presents a super-resolution image reconstruction method based on simulated annealing algorithm optimizing,Firstly,the image is divided into multiple sub blocks,in which normalized intensity and coefficients of the wavelet transform coefficients are extracted,and then image feature vector is extracted by manifold learning algorithm,and simulated annealing algorithm is used to optimize the value of K,finally,the gradient of the high resolution image is used as the target gradient domain to realize the super- resolution image reconstruction through weight matrix,The results show that the proposed method is effective and efficient,its visual effect and objective evaluation results are better than other super resolution image reconstruction methods.