Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

Abstract:

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1389-1393
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1389
• Online since: September 2013
• Authors: Ren Tao Zhao, You Yu Wang, Hua De Li, Jun Tie
• Keywords: Evolutionary Computation, Incomplete Beta Function, Infrared Image Contrast Enhancement, Particle Swarm Optimization (PSO)

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