SAR image despeckling algorithm using enhanced edge detection in bandelet domain
Zhihui Xin, MengTing Yuan, ZhiXu Wang, Yu Sun, Jiayu Xuan, Wei Ma, Yongxin Li · 2022
With all-weather, all-time imaging characteristics of synthetic aperture radar (SAR), SAR image is applied widely. However, because of the SAR imaging mechanism, speckle noise is inevitably present in SAR images. In the translationinvariant second-generation bandelet transform (TIBT) domain, SAR image despeckling algorithm using edge detection and feature clustering (CFCM-TIBT) combines edge detection and fuzzy C-main clustering (FCM) operation is an efficient way to reduce speckle noise. However, the edges will be blurred by the algorithm. In order to improve the edge preservation ability and reduce false edge phenomenon. A new algorithm, named enhanced edge detection for SAR images despeckling in TIBT domain (CRFCM-TIBT), is proposed in this letter. It combines CFCM-TIBT and an improved edge detector, named C-RBED, which consists of Canny edge detector and rate-based edge detector (RBED). CRFCM-TIBT better realizes the extraction and separation of details that benefits from the ability of eliminating false edge pixels of C-RBED. The process of CRFCM-TIBT: C-RBED is first utilized to calculate and compare edge direction map (EDM) and edge strong map (ESM) several times. Then, TIBT and FCM are used to decompose and despeckle the edge-removed image, respectively. Finally, add the removed edges to the reconstructed image. In the two scenarios of Bedfordshire and Horse track, the Equivalent Look Number (ENL) and Edge Preservation Index (ESI) of this algorithm are better than traditional Lee filtering, FCM-TIBT and CFCM-TIBT. The experimental results show from the evaluation indicators and visual effects that the method proposed in this paper significantly improves ESI while ensuring a better ENL.