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摘要: 为了更有效地实现高噪声环境下的图像去噪, 本文提出一种基于深度学习的高噪声图像去噪算法.该算法首先采用递增扩充卷积并且融合批量标准化和Leaky ReLU函数对输入含噪图像进行特征提取与学习; 然后通过结合递减扩充卷积和ReLU函数对提取的特征进行图像重构; 最后通过整合残差学习和批量标准化的端到端网络实现图像与噪声的有效分离.实验结果表明, 本文提出的算法不仅能够有效地去除高噪声环境下的图像噪声, 获得更高的峰值信噪比(Peak signal-to-noise ratio, PSNR)与结构相似度(Structural similarity index, SSIM), 而且还能够有效地改善图像的视觉效果, 具有较好的实用性.Abstract: In order to perform image denoising in high-noise environment more effectively, a high-noise image denoising algorithm based on deep learning is proposed in this paper. Firstly, the proposed algorithm utilized increased expanded convolutional and combined the batch normalization and Leaky ReLU function to extract and learn for the features of noisy image. Secondly, the extracted feature via the decreased expansion convolution and ReLU function for image reconstruction. Finally, the effective separation of image and noise is realized by end-to-end network of integrating the residual learning and batch standardization. The experimental results illustrated that the proposed algorithm removed the image noise more effectively and obtain higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) under high noise environment. In addition, the proposed algorithm also significantly improved the visual effects of images, and had a good practicality.
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Key words:
- Deep learning /
- image denoising /
- convolutional neural networks (CNN) /
- residual learning /
- batch normalization
1) 本文责任编委 杨健 -
图 3 不同的去噪方法对lighthouse的去噪效果对比图
Fig. 3 Comparison of denoising effects of lighthouse with different denoising methods
图 4 不同的去噪方法对butterfly的去噪效果对比图
Fig. 4 Comparison of denoising effects of butterfly with different denoising methods
表 1 不同去噪算法在BSD68数据集下的峰值信噪比(PSNR) (dB)
Table 1 The PSNR value using different denoising algorithms at the BSD68 data set (dB)
$\sigma$ BM3D WNNM MLP TNRD DnCNN EPLL CSF 特定噪声模型 随机噪声模型 15 31.07 31.37 – 31.42 31.73 31.21 31.24 31.94 31.85 25 28.57 28.83 28.96 28.92 29.23 28.68 28.74 29.46 29.38 40 26.22 26.33 – 26.49 26.88 26.26 26.30 27.11 27.06 50 25.62 25.87 26.03 25.97 26.23 25.67 – 26.48 26.47 60 23.18 – 23.55 23.43 23.73 23.24 23.27 24.01 24.06 
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表 2 不同去噪算法在BSD68数据集下的结构相似度
Table 2 The SSIM value using different denoising algorithms at the BSD68 data set
$\sigma$ BM3D WNNM MLP TNRD DnCNN 本文方法1 本文方法2 15 0.8772 0.8774 0.8792 0.8826 0.8826 0.8831 0.8827 25 0.8017 0.8019 0.8120 0.8157 0.8190 0.8193 0.8190 40 0.7223 0.7237 0.7294 0.7310 0.7322 0.7334 0.7331 50 0.6869 0.6871 0.6956 0.7029 0.7076 0.7102 0.7100 60 0.6521 0.6544 0.6643 0.6712 0.6745 0.6796 0.6799
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表 3 不同尺寸大小的测试图像去噪运行时间比较($\sigma = 25$) (s)
Table 3 The running time of test images denoising with different size ($\sigma = 25$) (s)
图像块大小(像素) 配置 BM3D WNNM TNRD MLP EPLL CSF DnCNN 特定噪声模型 随机噪声模型 $256\times 256$ CPU/GPU 0.65 203.1 0.45/0.010 1.42 25.4 2.11/– 0.74/0.014 0.68/0.016 0.97/0.020 $512\times 512$ CPU/GPU 2.85 773.2 1.33/0.032 5.51 45.5 5.67/0.92 3.41/0.051 2.98/0.072 3.68/0.083 $1 024\times 1 024$ CPU/GPU 11.89 2 536.4 4.61/0.116 19.4 422.1 40.8/1.72 12.1/0.200 10.7/0.160 13.7/0.173
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