SAR Image De-noising Based on Residual Image Fusion and Sparse Representation

Abstract

Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

“…With the rapid development of complementary metal oxide semiconductor (CMOS) technologies, CMOS image sensors have become popular with consumer and vehicle electronics, telemedicine, video surveillance, space exploration, fluorescence detection, and so on [1][2][3][4]. However, images generated by these sensors inevitably contain noise, owing to their internal structure, which results in image quality degradation [5,6], and thus, estimating these noise parameters accurately assumes paramount importance in improving the performance of denoising algorithms [3,4,[7][8][9]. For CMOS image sensors, a signaldependent noise model, such as the Poisson-Gaussian model, can more accurately delineate the noise characteristics than an additive channel-dependent noise model [10][11][12][13][14][15][16][17][18][19].…”