稀疏度蒸馏图像去噪

Q1 Computer Science
S. Kawata, Nao Mishima
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引用次数: 1

摘要

提出了一种基于收缩的图像去噪方法。在该方法中,将输入图像中的小块投影到使投影系数稀疏的空间中,并使用显式评估的稀疏度来控制收缩阈值。平均而言,与图像去噪领域的一种最新方法相比,该方法获得了更高的定量评价值(psnr和ssim)。该方法可以有效地去除自然图像中的随机噪声,同时保留复杂的纹理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Denoising with Sparsity Distillation
We propose a new image denoising method with shrinkage. In the proposed method, small blocks in an input image are projected to the space that makes projection coefficients sparse, and the explicitly evaluated sparsity degree is used to control the shrinkage threshold. On average, the proposed method obtained higher quantitative evaluation values (PSNRs and SSIMs) compared with one of the state-of-the-art methods in the field of image denoising. The proposed method removes random noise effectively from natural images while preserving intricate textures.
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来源期刊
IPSJ Transactions on Computer Vision and Applications
IPSJ Transactions on Computer Vision and Applications Computer Science-Computer Vision and Pattern Recognition
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