A Sine-Cosine Optimizer-Based Gamma Corrected Adaptive Fractional Differential Masking for Satellite Image Enhancement

The prime objective is to harvest more and more information present in a remotely sensed dark satellite image, captured under poorly illuminated circumstances. For imparting optimal quality enhancement, a recently proposed and highly efficient Sine-Cosine optimizer is employed in association with a novel optimally weighted gamma corrected (GC) fractional differential (FD) order masking framework. Overall texture enhancement is achieved by optimally ordered FD masking along with its optimal augmentation with GC interim channel. Core objective of entropy enhancement is fulfilled by keeping a proper check for over-enhanced or saturated regions through the introduction of penalty term in the employed cost function, for adaptive exploration and identification of missing levels for more optimal redistribution throughout the permissible range; so that natural look can be preserved efficiently. Rigorous experimentation is performed by employing performance evaluation and comparison with preexisting highly appreciated quality enhancement approaches.