Adaptive Total Variation Speckle Denoising Method Based on Sine-Cosine Decomposition

Ji, 刘吉 Liu; Xiaohui, 黄晓慧 Huang; Jinhui, 武锦辉 Wu; Ninggang, 苏凝钢 Su; Lixia, 于丽霞 Yu

doi:10.3788/cjl202047.1004004

Cited by 4 publications

(1 citation statement)

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“…Rudin et al [11] proposed a total variation (TV) model for denoising, which achieved good results in speckle noise removal despite the staircasing effect it produced. In recent years, literature [12][13][14] has made significant improvements and optimizations to the TV denoising algorithm. By minimizing the staircasing effect while preserving image edge information, the improved algorithm model maintains its adaptability.…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar images denoising based on multi-scale attention cascade convolutional neural network

Shan¹,

et al. 2023

Meas. Sci. Technol.

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Synthetic aperture radar (SAR) images are often affected by speckle noise, which can hinder accurate interpretation and subsequent use of the images in applications such as target detection and segmentation. To address this issue, we propose a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MSAC-Net). Our algorithm employs multi-scale asymmetric convolution to extract image features and an attention mechanism to integrate these features. Additionally, we designed a multi-layer deep cascade convolutional network to enhance the generalization ability of the model features. Experimental results show that our proposed MSAD-Net model significantly outperforms state-of-the-art SAR image denoising algorithms. Specifically, it achieves a significant improvement in peak signal-to-noise ratio (PSNR), with an increase of about 0.81~13.97dB, and structural similarity index (SSIM), with an increase of about 0.01~0.14. Overall, our study presents a novel denoising algorithm for SAR images that greatly improves the accuracy of subsequent image applications.

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Section: Introductionmentioning

confidence: 99%