Notice of Violation of IEEE Publication Principles: A Differential Evolution Algorithm for Image Fusion

Notice of Violation of IEEE Publication Principles

"A Differential Evolution Algorithm for Image Fusion"
by P. Pardhasaradhi, T. Nagarjuna, P. Seetharamaiah
in the 2011 International Conference on Process Automation, Control and Computing (PACC), 2011, pp. 1 – 6.

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 contains significant portions of original text from the paper cited below. The original text was copied with insufficient 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:

"Fusion of multi-focus images using differential evolution algorithm"
by V. Aslantas, R. Kurban
in Expert Systems and Applications, 2010, pp. 8861 – 8870.


Image fusion is an integral part of many existing and future surveillance systems. Due to the limited depth-of-focus of optical lenses (especially such with long focal lengths) it is often not possible to get an image which contains all relevant objects in focus. One way to get an everywhere-in-focus image is to fuse the images of the same scene which are taken with different focal settings. This paper describes a novel optimal method for multi-focus image fusion using differential evolution algorithm. The source images are first decomposed into blocks. Then, the sharper blocks are selected by employing a sharpness criterion function. The selected blocks are finally combined to construct the fused image. The motivation of the proposed method lies in the fact that an optimized block size could be more effective than a fixed block size. The experimental results show that the proposed method can perform better than the other traditional methods in terms of both quantitative and visual evaluations.