Piecewise Gamma Corrected Weighted Framework for Fuzzified Dynamic Intensity Equalization for Optimal Image Enhancement

In this paper, a new fusion-based image enhancement framework is proposed by harvesting the swarm intelligence of Levy Firefly optimizer. Piece-wise gamma corrected interim intensity channels are fused along with dynamic fuzzy equalization based channel. Thus, quality enhancement of the poorly acquired and/or dark image is achieved in this paper by an optimally weighted framework. Over-ranged intensity levels are reduced by the penalty term inclusion in the entropy-dependent fitness function. Thus, the constructive amalgamation of gamma-compressed, gamma-expanded and fuzzy-equalized channel is attained through an evolutionary approach. Due to the presence of these three interim channels, the desired image quality enhancement (contrast as well as entropy enhancement along with sharpness and colorfulness enhancement of the images) is achieved. The simulation results and their comparative analysis (with various state-of-the-art methodologies) authenticate the outperforming excellence of the proposed approach.