Adaptive Filter using Improved Pigeon Inspired Optimization Algorithm for Satellite Image Denoising

Satellite imaging is a current development in image processing; however, it faces a lot of challenges because of the environmental factors. For denoising, state-of-the-art method has developed some filters like the hyperspectral satellite images, which is not effectual. Moreover, this paper proposed an adaptive filter using the assist of an optimization approach for the satellite image denoising. The developed adaptive filter performs the image denoising via noise correction, noise identification, and image enhancement. In the satellite image by transforming the image to a binary image, the type-2 fuzzy filter recognizes the noisy pixels which are passed via the adaptive non-local mean filter for the noise correction. Subsequently, the kernel-based interpolation scheme performs the image enhancement, which is performed through the developed improved Pigeon optimization algorithm (IPOA). The whole experimentation of the developed denoising system is performed taking into consideration by satellite images from standard databases. It is obvious that the developed adaptive filter with the developed improved Pigeon optimization algorithm has enhanced performance with the PSNR values from the outcomes.