A Comprehensive Review on Soft Computing Techniques for Speckle Reduction in Synthetic Aperture Radar (SAR) Imagery

Speckle is an inherent multiplicative noise in coherent imaging systems such as synthetic aperture radar (SAR), and it reduces radiometric resolution, obscures weak scatterers, and degrades downstream tasks (e.g., detection, segmentation, and classification). Over the last four decades, despeckling has evolved from local statistical filters (e.g., Lee/Frost/Kuan families) to variational and nonlocal approaches, and more recently to data‑driven deep networks. Within this landscape, soft computing—broadly covering fuzzy logic, neural computing, and evolutionary/swarm optimization—has been increasingly used to address the central despeckling dilemma: suppress speckle while preserving edges, textures, and small man‑made targets. This review organizes soft computing techniques for SAR despeckling into: (i) fuzzy and neuro‑fuzzy filtering; (ii) neural and deep learning models (CNN, autoencoders, GANs, and diffusion models); and (iii) evolutionary and swarm optimization used for parameter tuning, transform-domain thresholding, and multi‑objective decision making. Particular emphasis is placed on multi‑objective formulations that jointly optimize PSNR and MSSIM/SSIM, yielding Pareto‑optimal trade‑off solutions (e.g., MOPSO-based threshold selection). We summarize datasets, evaluation metrics, and reproducibility concerns, and provide a comparative synthesis of strengths, limitations, and research gaps. Finally, we outline actionable future directions, including self‑supervised despeckling on real SAR, objective‑function design aligned with task performance, uncertainty quantification, and standardized evaluation protocols.