A Levy Flight Firefly Optimizer based Piecewise Gamma Corrected Unsharp Masking Framework for Satellite Image Enhancement

In this paper, an efficiently modified lévy-flight based biologically inspired firefly optimizer is employed in association with a novel optimally weighted piecewise gamma corrected unsharp masking framework for imparting overall quality improvement of remotely sensed dark satellite images. The key intelligence is to utilize a weighted summation of intensity as well as texture based enhancement along with an efficiently defined cost function. The cost function is framed such that more and more intensity span can be explored in a positive manner. Here, the unsharp masking takes care for enhancing the high frequency content of the images. In association with it, piecewise gamma correction is also imparted to enhance the intensity channel of the input image. Rigorous experimentation is executed by employing the performance evaluation and comparison with pre-existing recently proposed and highly appreciated quality enhancement approaches.