Notice of Violation of IEEE Publication Principles: Image inpainting with group based sparse representation using self adaptive dictionary learning

Notice of Violation of IEEE Publication Principles

"Image Inpainting with Group Based Sparse Representation using Self Adaptive Dictionary Learning,"
by T J V Subrahmanyeswara Rao, M Venu Gopala Rao, T V N L Aswini,
in the Proceedings of the International Conference on Signal Processing And Communication Engineering Systems (SPACES), January 2015, pp.301-305

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 large portions of text and figures 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:

"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, May 2014, pp. 3336 – 3351


Inpainting is an imaging technique that modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal / replacement of selected objects. The popularity of sparse representation and compressed sensing makes the sparse priors to be considered for solving inpainting problems. In earlier works, the patch of an image is taken to be sparse in a particular basis, which is called Patch based sparse representation. The patch based modelling suffers from two severe problems. In our work we exploit the concept of Group based sparse representation (GSR), which takes group (composed of nonlocal patches with similar structures) as the basic unit instead of patch. The GSR sparsely represents natural images in the domain of group, which results intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework Under the same roof, an efficient self-adaptive dictionary learning method is designed for each group with low complexity, rather than learning the dictionary from natural images.