Efficient noise filtering method based on variational image decomposition for interferometric synthetic aperture radar phase images

The denoising of InSAR phase images with fringes of great density variety and high noise level is particularly challenging. In this paper, an efficient technique based on variational image decomposition is proposed to remove noise from an InSAR phase image. We propose a new image decomposition model BL-Hilbert-BM3D to decompose an InSAR phase image into three components: fringes of low density, fringes of high density and noise. They are described by Beppo Levi space (BL), Hilbert space and Block Matching and 3D function space (BM3D) respectively. So our model is able to sufficiently smooth fringes of low density as well as perfectly preserve fringes of high density. We test the proposed method on a simulated and an actual InSAR phase images. We compare the results yielded by our method with those by four other widely used and well-known methods in terms of both quantitative evaluation and visual quality. The experimental results have demonstrated the validity of the proposed method.