An Adaptive SAR Despeckling Method Using Cuckoo Search Algorithm

Abstract

Despeckling of SAR imagery is a crucial step prior to their automated interpretation as information extraction from noisy images is a challenging task. Though a huge despeckling literature exists in this regard, there is still a room for improvement in existing techniques. The contemporary despeckling techniques adversely affect image edges during the noise reduction process and are thus responsible for losing the significant image features. Therefore, to preserve important features during the speckle reduction process, a two phase hybrid despeckling filter is proposed in this study. The first phase of the hybrid filter focuses on edge preservation by employing a new edge detection criterion for the guided filter. Whereas the second phase attempted to suppress speckle by utilizing some speckle suppression and edge preservation filters whose sequence is determined by the cuckoo search optimization algorithm (CSO). The CSO generates optimal sequences of these filters according to the nature of input images with peak signalto-noise ratio (PSNR) and structural similarity index (SSIM) as its objective function. Performance comparison of the proposed hybrid filter with state-of-the art techniques has revealed its best despeckling behavior on standard and real SAR images.