Satellite Image Denoising Using Optimized Pulse Coupling Network

Satellite image denoising is of utmost importance in various applications weather monitoring, flood control and crop monitoring focusing on enhancing the visual quality of affected images. Denoising is a contemporary trend in the application of image processing, yet encounters numerous restrictions attributed to environmental conditions. In recent times, there has been extensive exploration of DL (deep learning) methods for the purpose of noise removal in satellite images. This work presents a DL based optimization model for Satellite image denoising. That is the DL model PCN (pulse coupling network) with AF (artificial flora) optimizer is used for parameter enhancement in PCN. PSNR and SSIM values achieved are 44.8 and 0.89 respectivelyon the FloodNet database. Thus, the analysis yielded superior performance and achieved better quality images.