Notice of Violation of IEEE Publication Principles: Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution

The following message is relayed from an update made on IEEE Xplore. Notice of Violation of IEEE Publication Principles "Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution" by Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, and Zhongbo Zhang in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 705-713 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 copied portions of text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper titles) and without permission. "Group-Based Sparse Representation for Image Restoration" by Jian Zhang, Debin Zhao, and Wen Gao in the IEEE Transactions on Image Processing, Vol 23, No 8, August 2014, pp. 3336-3351 "Dual-domain Image Denoising" by Claude Knaus, Matthias Zwicker in the Proceedings of the IEEE International Conference on Image Processing, (ICIP), September 2013, pp. 440-444 "A Machine Learning Approach for Non-blind Image Deconvolution" by Christian Schuler, Harold Christopher Burger, Stefan Harmeling, and Bernhard Scholkopf in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013, pp. 1067-1074 Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fine scale texture structures while removing blur and noise. Various methods have been implemented in both spatial and transform domains, such as gradient based methods, nonlocal self-similarity methods, sparsity based methods. However, each domain has its advantages and shortcomings, which can be complemented by each other. In this work