摘要
传统的基于学习的超分辨率算法普遍采用样本库来训练字典对,训练时间长且对样本库依赖较大。针对传统算法的不足,提出一种新的单张彩色图像超分辨率算法。该方法基于稀疏编码超分辨率模型,利用图像自相似性和冗余特性,并结合图像金字塔结构,采用低分辨率图像本身来训练高、低分辨率图像块的字典对。同时,针对彩色图像,该算法采用一种基于稀疏表示的彩色图像存储技术,将彩色图像的三通道值组合成一个向量进行图像稀疏处理,以更好地维持原始图像细节信息。实验结果表明,与传统的超分辨率算法相比,该算法不但有更好的视觉效果和更高的峰值信噪比(PSNR),而且计算速度快。
Traditional learning-based super-resolution algorithms generally adopt training images for learning dictionary pairs, they are time-consuming, and the results strongly depend on the training images. To address these problems, a new super-resolution approach from a single color image was proposed based on sparse coding model. According to image self- similarity and redundancy features, this algorithm utilized low-resolution image itself for training dictionary pairs, combined with image pyramid structure. Meanwhile, in view of color images, the sparse representation based color image storage technology was used, which concatenated the values of three channels to a single vector and directly represented them sparsely. The experimental results illustrate that the proposed method not only can generate high-resolution images with better visual effects and higher Peak Signal-to-Noise Ratio （PSNR） but also has less computation time.
出处
《计算机应用》
CSCD
北大核心
2013年第2期472-475,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61173182
61179071)
四川省国际科技合作与交流研究计划项目(2012HH0004)
四川省应用基础项目(2011JY0124)
