Abstract
In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.
Highlights
Synthetic aperture radar (SAR) remote sensing has been extensively applied in military and civil fields because of the all-day, all-weather acquisition capability
To demonstrate the efficiency of our method, we compare it with other state-of-the-art speckle removal algorithms, such as the Frost filter (Frost) [6], the original non-local weighted group low-rank representation (WGLRR) [31], the blind de-noising algorithm based on weighted nuclear norm (BWNNM) [32], K-SVD [17] and the nonlocal fast adaptive nonlocal SAR de-noising (FANS) [25]
The methods involved in this paper shows from a minimum of about 0.77 for WGLRR, through 0.87 for FANS and 0.89 for the proposed method, to a maximum of 0.92 for BWNNM which indicates that the proposed method has the smaller bias
Summary
Synthetic aperture radar (SAR) remote sensing has been extensively applied in military and civil fields because of the all-day, all-weather acquisition capability. As a popular denoising method, total variation (TV) regularization is used in the multiplicative noise by some scholars [7,8]. Wavelet shrinkage can be readily applied to SAR despeckling after a homomorphic transformation. Nonlocal (NL) filtering has been successfully applied to SAR images denoising in the wavelet domain [24,25,26]. We propose a boosting SAR image despeckling method based on non-local weighted group low-rank representation (WGLRR). Grouping the similar patches by an ad hoc measure to form groups of similar image blocks, we use the low rank representation model for speckle noise reduction of the similar image blocks.
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