基于自相似性与稀疏表示的超分辨率算法

目的为了解决当前稀疏表示的超分辨率算法效果依赖参与训练的数据的问题,结合图像的自相似性,提出一种基于自相似性与稀疏表示相结合的超分辨率算法。方法算法利用图像的多维自相似性,构建多维图像金字塔,采用改进的相似块搜索策略,得到对应的高低分辨率图像块作为训练样本,然后对样本进行字典训练,最后根据稀疏表示得到超分辨率图像。结果实验结果显示,文中算法在峰值信噪比(PSNR)和结构相似度(SSIM)上优于其他算法,对于实验图像而言,PSNR平均提升了0.5 dB。结论提出的超分辨率算法未引入外部数据库,具有较好的效果,能够用于超分辨率重建。

Super-Resolution Algorithm Based on Self-similarity and Sparse Representation

The paper aims to propose a super-resolution algorithm based on the self-similarity and sparse representation in combination with the self-similarity of images to solve the problem that the effect of the current sparse representation super-resolution algorithm depends on the training data. In the algorithm, the multi-dimensional self-similarity of images was used to construct amulti-dimensional image pyramid, and the improved similarity block search strategy was used to obtain the high and low resolution image blocks as training samples. The dictionary training was carried out to the samples. Finally, the super-resolution image was obtained according to sparse representation. The experimental results showed that the proposed algorithm was superior to other algorithms in peak signal to noise ratio (PSNR) and structural similarity (SSIM). For the experimental images, the average PSNR was increased by 0.5 dB. The proposed super-resolution algorithm does not need external database and has a good effect. It can be used for super-resolution reconstruction.