Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

中文翻译：

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基于模糊相似度融合的单张医学图像正则化超分辨率恢复算法

由于采集，传输和存储中的各种原因，医学图像会模糊不清并产生噪点。为了提高医学图像的恢复质量，提出了一种基于模糊相似度融合的常规超分辨率恢复算法。基于在多个尺度上保持的相似度，通过模糊相似度融合来计算医学图像的融合相似度。首先，模糊相似性由区域特征决定。根据最大值获得具有一定相似性的图像，并通过所有明显的区域特征获得融合图像。然后，采用自适应正则化恢复算法。为了确保目标函数具有全局最优解，对非线性函数的全局最小解的正则化参数进行迭代求解。最后，实验结果表明，恢复图像的均方误差（MSE）和峰值信噪比（PSNR）得到了明显改善。恢复的图像在局部边缘的毛刺方面也有明显的改善。此外，该算法具有良好的稳定性，并具有显着增强的PSNR。