Towards Better Understanding of SAR Image: Feature Enhancement via Non-Local and Low-Rank Approach

Abstract

Feature enhancement for synthetic aperture radar (SAR) images is of great significance for their understanding and interpretation. In this work, we aim to address the issues by introducing the low-rank constraint into non-local means framework, dubbed NL_LR. The non-local means framework takes advantages of the non-local self-similarity of SAR images, which makes this approach efficient in noise suppression and preservation of structures and resolution. When estimating the value of the target pixel, a low-rank matrix can be constructed with vectorization of similar image patches. By exploiting this low-rank prior of patch matrix and decomposition of sparse and low-rank matrices, the denoised low-rank patch matrix is more accurate which will also increase the accuracy of feature enhancement. Afterwards, the numerical algorithm is designed. Numerical experiments on the real-data of SAR images show that our novel method can reduce the noise in homogeneous areas especially speckle noise efficiently, preserve the structural feature, especially edges and textures and improve the resolution at the same time. Visually, the result of the proposed method is obviously improved.