Super-resolution reconstruction algorithm based on deep learning mechanism and wavelet fusion
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摘要:
深度学习技术在超分辨率重建领域中发展迅速。为了进一步提升重建图像的质量和视觉效果，针对基于生成对抗网络（GAN）的超分辨率重建算法重建图像的纹理放大后不自然的问题，提出了一种结合小波变换和生成对抗网络的超分辨率重建算法。所提算法在生成对抗网络中将小波分解的每个分量在各自独立的子网中进行训练，实现网络对小波系数的预测，有效地重建出具有丰富的全局信息和局部纹理细节信息的高分辨率图像。实验结果表明，对比基于生成对抗网络的算法，所提算法重建图像的客观评价指标峰值信噪比（PSNR）和结构相似性分别能提高至少0.99 dB和0.031。
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关键词:
- 小波变换 /
- 生成对抗网络(GAN) /
- 超分辨率重建 /
- 深度学习 /
- 多分辨分析
Abstract:Deep learning technology has developed rapidly in the field of super-resolution reconstruction. In order to further improve the quality and visual effect of reconstructed images, this paper proposes a super-resolution reconstruction based on wavelet transform and generative adversarial networks (GAN) for the unnatural problem of texture reconstruction based on the super-resolution reconstruction algorithm of GAN. In this paper, each component of the wavelet decomposition in the GAN is trained in separate subnets to realize the prediction of wavelet coefficients by the network. Effectively reconstruct high-resolution images with rich global information and local texture details. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity of the objective evaluation index of the reconstructed image can be improved by at least 0.99 dB and 0.031, respectively, based on the algorithm of GAN.
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图 4 Set14数据集中图像“barbara”测试结果
Figure 4. Test results of "barbara" image in Set14 dataset
图 5 Set14数据集中图像“monarch”测试结果
Figure 5. Test results of "monarch" image in Set14 dataset
表 1 PSNR和SSIM的测试结果
Table 1. Test results of PSNR and SSIM
数据集 PSNR/dB, SSIM SRGAN 本文算法 Set5 29.50，0.841 30.63，0.897 Set14 25.95，0.740 26.94，0.771 BSD100 25.16，0.669 26.91，0.728 
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表 2 FSIM和UIQ的测试结果
Table 2. Test results of FSIM and UIQ
数据集 FSIM，UIQ SRGAN 本文算法 Set5 0.907，0.925 0.929，0.935 Set14 0.891，0.973 0.900，0.978 BSD100 0.828，0.981 0.832，0.984
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表 3 重建图像LBP特征图与原始图像LBP特征图之间方差
Table 3. Variance between LBP feature map of reconstructed image and LBP feature map of original image
图像 方差 SRGAN 本文算法 bird 0.124 0.061 barbara 0.261 0.216 monarch 0.187 0.121
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