改进相似性度量模型的单幅图像自学习超分辨算法

Single Image Super-Resolution from Local Self-examples Based on an Improved Similarity Measurement Model

在自学习超分辨算法中,高低分辨率图像块匹配是否准确是算法的关键。在高低分辨率图像块匹配过程中,考虑图像块纹理结构的重要性,提出了一种基于纹理约束的图像块相似性度量模型,应用该模型完成了高低分辨率图像块更为准确的匹配,使超分辨结果图像的细节更加丰富,进一步提高了图像质量。该算法仅使用了单幅低分辨率图像自身的相关先验信息,有效提升了图像的空间分辨率。实验结果表明,与双三次插值算法、自相似学习超分辨算法相比,本文提出的算法超分辨视觉效果更好,并且在客观评价指标中同样表现良好。

The accurate matching of high and low resolution image blocks is the key of self-examples super resolution algorithm.In the process of blocks matching of high and low resolution images,considering the importance of texture image block structure,a similarity metric model based on constrained texture image patch is proposed in this paper.By using this exact matching model,the detail of super-resolution result image is further enriched,and the image quality is improved also.The new algorithm has the particular advantage of improving spatial resolution of image only using prior information of single low-resolution image itself.The experimental results show that the proposed algorithm has a better super-resolution visual effect compared with the bicubic interpolation algorithm and the local self-examples super-resolution algorithm,and it also has a good performance in the objective evaluation index.