Notice of Violation of IEEE Publication Principles: Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features

Notice of Violation of IEEE Publication Principles

“Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features”
by Yuantao Chen, Jin Wang, Xi Chen, Mingwei Zhu, Kai Yang, Zhi Wang, and Runlong Xia in IEEE Access, April 2019

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper is a translation and duplication of the content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

“Single image super resolution algorithm based on structural self-similarity and deformation block feature”
by Wen Xiang, Ling Zhang, Yunhua Chen, Qiumin Ji
in the Journal of Computer Applications (39) 1, June 2018


To solve the problem of insufficient sample resources and poor noise immunity in single-image super-resolution (SR) restoration procedure, the paper has proposed the single-image SR algorithm based on structural self-similarity and deformation block features (SSDBF). First, the proposed method constructs a scale model, expands the search space as much as possible, and overcomes the shortcomings caused by the lack of a single-image SR training sample; Second, the limited internal dictionary size is increased by the geometric deformation of the sample block; Finally, in order to improve the anti-noise performance of the reconstructed picture, a group sparse learning dictionary is used to reconstruct the pending image. The experimental results show that, compared with state-of-the-art algorithms such as bicubic interpolation (BI), sparse coding (SC), deep recursive convolutional network (DRCN), multi-scale deep SR network (MDSR), super-resolution convolutional neural network (SRCNN) and second-order directional total generalized variation (DTGV). The SR images with more subjective visual effects and higher objective evaluation can be obtained through the proposed method. Compared with existing algorithms, the structural network converges more rapidly, the image edge and texture reconstruction effects are obviously improved, and the image quality evaluation, such as peak signal-noise ratio (PSNR), root mean square error (RMSE), and structural similarity (SSIM), are also superior and popular in image evaluation.