A Study on the Validity and Scope of Self-Similarity Property in Super-Resolution of Medical Images

Super-resolution is an ill-posed inverse problem. It needs a self-similarity regularization term in its formulation to solve the issue. Researchers have studied self-similarity in natural images thoroughly and extended the results to medical data. This paper investigates the self-similarity property using a general affine model and the SSIM, PSNR, matrix rank, and mutual information measures. We study variations of self-similarity with image modality, inter/intra-resolution, tissue types, and acquisition phase. Based on our experiments, super-resolution algorithms that use self-similarity property achieve better results in CT images than MR data. Some organs, including the liver, have a stronger self-similarity compared to the brain and lung. The extension of our study prepares a comprehensive guide map to employ self-similarity properties in medical image processing algorithms.