Chang K, Ding P L K, Li B. “Single Image Super Resolution Using Joint Regularization”, IEEE Signal Processing Letters, 2018, 25(4): 596-600.


Single Image Super-resolution Using Joint Regularization

Kan Chang, Pak Lun Kevin Ding, Baoxin Li



This letter proposes a reconstruction-based single image super resolution (SR) method by using joint regularization, where a group-residual-based regularization (GRR) and a ridge-regression-based regularization (3R) are combined. In GRR, non-local similar patches are grouped together, and the group weights are calculated so as to adaptively constrain the residual values in the gradient domain. In 3R, we adopt the ridge-regression-based method to establish the projection matrices from an external high-resolution (HR) training set, so that the external HR information can be utilized. To obtain an estimation of the targeted HR image, an efficient algorithm is designed for solving the joint formulation. Experimental results on different image datasets indicate that the proposed method is able to achieve the state-of-the-art performance.



Index Terms—Super Resolution, Non-local Self-similarity, Total Variation, Ridge Regression, Regularization.



Source Code

To facilitate further evaluation and exploration of the method proposed in the above paper, we publish the source code at this link.

You are free to use the source code provided that (1) you clearly cite the source; and (2) you do not make any redistribution of the code.