Piecewise Gamma Corrected Optimally Framed Grumwald-Letnikov Fractional Differential Masking for Satellite Image Enhancement

摘要

In this paper, a highly efficient biologically inspired Lévy-flight firefly algorithm based optimally weighted piecewise-gamma-corrected Grumwald-Letnikov (GL) fractional differential (FD) masking is presented for quality enhancement of densely textured, 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, an efficient fractional order differentiation based unsharp masking, takes care for enhancing the texture content of the images along with desired restoration of all kinds of local edges. 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.