A Proficient Cascaded Filter Design for Image Denoising by Arithmetic Optimization Algorithm

This paper describes the design of a competent cascaded filter for image denoising using Arithmetic optimization Algorithm (AOA). Though several conventional spatial domain filters are used individually to perform denoising, but fails to give satisfactory results for various types of available noises like Gaussian, Salt & Pepper, and Speckle. Thus, cascading multiple spatial filters by means of AOA algorithm shows upgraded outcome compared to existing methods. The comparative study is carried out among popular spatial filters and the proposed cascaded filter through objective and subjective analysis. The quality measurement parameters like IQI, PSNR, and SSIM shows significant improvement for various noises and upgraded visual attributes in reconstructed image. Furthermore, the comparative graphical investigation of quality parameters for different denoising methods against various noise variance levels shows it is well designed to resist low- and high-density noises. Thus, it is quite apparent that our proposed design provides an optimal solution compared to conventional denoising methods.