Multi - focus image fusion using self - resemblance measure. (October 2018)

• Record Type: Journal Article
• Authors: Paramanandham, Nirmala; Rajendiran, Kishore
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 13
• Page End: 27
• Publication Date: 2018-10
• Subjects: Block separation -- Complexity -- Image fusion -- Multiple objective particle swarm optimization -- Self resemblance measure; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. Inspired by the pixel correlation property, a fusion rule based on self-resemblance measure (SRM) is proposed. An efficient image fusion algorithm is presented for fusing multi-focus images using self resemblance measure, consistency verification and multiple objective particle swarm optimization. Initially based on SRM fusion rule, the source images are fused and then the block sizes are adaptively selected by particle swarm optimization based on the multiple objective fitness functions for obtaining enhanced fused image. The proposed framework is evaluated using quantitative metrics such as root mean square error, peak signal to noise ratio, correlation, standard deviation, mutual information and Petrovic metric. Experimental results demonstrate the outperformance of the proposed algorithm over many other well known state-of-the-art fusion techniques, both in visual effect and objective evaluation criteria.