In this paper, a method is proposed to achieve a high-resolution image from a low-resolution image. Because of the ill-posedness of the super-resolution problem, sparsity constraint is used as a prior, in this work. On the one hand, we use convolutional sparse representation on the whole image different from the patch-based method. On the other hand, we apply fewer filters even in smaller sizes for reconstructing the high-resolution image. Therefore, despite the reduced processing time, the reconstructed image quality is improved compared to the reference methods. In this work, the training images are different in terms of content from the testing images. Experimental results on a variety of MR images indicate improvement in the quality of the high-resolution MR image, in terms of qualitative and quantitative criteria.

中文翻译：

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基于卷积稀疏表示的单幅MR图像超分辨率

在本文中，提出了一种从低分辨率图像获得高分辨率图像的方法。由于超分辨率问题的不适定性，在这项工作中使用稀疏约束作为先验。一方面，与基于补丁的方法不同，我们在整个图像上使用卷积稀疏表示。另一方面，即使在较小尺寸的情况下，我们也使用较少的滤波器来重建高分辨率图像。因此，尽管减少了处理时间，但与参考方法相比，重建的图像质量得到了提高。在这项工作中，训练图像在内容方面与测试图像不同。在各种 MR 图像上的实验结果表明，就定性和定量标准而言，高分辨率 MR 图像的质量有所提高。