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摘要: 模糊图像的超分辨率重建具有挑战性并且有重要的实用价值. 为此, 提出一种基于模糊核估计的图像盲超分辨率神经网络(Blurred image blind super-resolution network via kernel estimation, BESRNet). 该网络主要包括两个部分: 模糊核估计网络 (Blur kernel estimation network, BKENet)和模糊核自适应的图像重建网络(Kernel adaptive super-resolution network, SRNet). 给定任意低分辨率图像(Low-resolution image, LR), 首先利用模糊核估计子网络从输入图像估计出实际的模糊核, 然后根据估计到的模糊核, 利用模糊核自适应的图像重建子网络完成输入图像的超分辨率重建. 与其他图像盲超分辨率方法不同, 所提出的模糊核估计网络能够显式地从输入低分辨率图像中估计出完整的模糊核, 然后模糊核自适应的图像重建网络根据估计到的模糊核, 动态地调整网络各层的图像特征, 从而适应不同输入图像的模糊. 在多个基准数据集上进行了有效性实验, 定性和定量的结果都表明该网络优于同类的图像盲超分辨率神经网络.Abstract: Blind blurred image super-resolution is challenging and has important application values. This paper proposes a blurred image blind super-resolution network via kernel estimation (BESRNet), which mainly includes two parts: Blur kernel estimation network (BKENet) and kernel adaptive super-resolution network (SRNet). Given a low-resolution image (LR), the network uses the blur kernel estimation subnetwork to estimate the blur kernel from the input image, and then it uses the kernel adaptive super-resolution subnetwork to super-resolve the input low-resolution image. Different from other blind super-resolution methods, the proposed blur kernel estimation subnetwork gives the whole blur kernel, then the kernel adaptive super-resolution subnetwork dynamically adjusts the image features of different network layers according to the estimated blur kernel to adapt to different image degradations. In this paper, extensive experiments are carried out on multiple benchmark datasets. The qualitative and quantitative results show that proposed method is superior to other blind super-resolution methods.
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图 9 不同基准数据集上模糊核估计结果的视觉效果对比
Fig. 9 Visual comparison of blur kernels estimated by different methods on different benchmark datasets
图 10 (×4) 使用真值模糊核作为先验情况下, 各个超分辨率方法的视觉效果对比, 放大观看效果更佳
Fig. 10 (×4) Visual comparison of different methods with real blur kernels as prior, zoom in for best view
表 1 各个超分方法在基准数据集上的性能对比(PSNR (dB)/SSIM)
Table 1 Performance comparison of different super-resolution methods on benchmark datasets (PSNR (dB)/SSIM)
方法 放大倍数 数据集 Set5[39] Set14[40] BSD100[41] Urban100[42] DIV2K_val[37] Bicubic × 2 25.76/0.800 23.73/0.699 24.15/0.681 21.51/0.670 25.73/0.776 RDN[14] × 2 28.03/0.840 25.20/0.713 25.44/0.697 23.04/0.699 27.93/0.807 RCAN[17] × 2 24.53/0.751 23.05/0.668 23.49/0.653 21.04/0.633 24.70/0.733 DRN[8] × 2 — — — — — HAN[19] × 2 24.45/0.714 22.90/0.650 23.29/0.634 20.91/0.615 24.54/0.708 RDNMD × 2 29.00/0.879 25.89/0.803 25.97/0.798 24.16/0.818 28.23/0.863 ZSSR[30] × 2 26.06/0.804 24.02/0.707 24.43/0.688 21.90/0.685 25.99/0.785 IKC[22] × 2 — — — — — BESRNet (本文) × 2 30.96/0.903 27.73/0.834 27.20/0.827 25.38/0.845 29.96 /0.886 Bicubic × 4 24.72/0.755 22.83/0.647 23.34/0.628 20.65/0.613 24.79/0.733 RDN[14] × 4 27.46/0.808 24.72/0.694 25.03/0.671 22.53/0.690 27.24/0.775 RCAN[17] × 4 22.83/0.619 21.62/0.548 22.16/0.541 19.77/0.521 23.25/0.619 DRN[8] × 4 23.07/0.679 21.92/0.596 22.50/0.580 20.07/0.562 23.96/0.683 HAN[19] × 4 22.65/0.603 20.81/0.524 22.09/0.536 19.33/0.497 22.83/0.605 RDNMD × 4 28.63/0.834 25.33/0.716 25.51/0.690 23.29/0.718 27.68/0.793 ZSSR[30] × 4 25.09/0.710 23.75/0.640 24.15/0.620 21.52/0.622 26.72/0.752 IKC[22] × 4 28.93/0.844 25.94/0.719 25.73/0.696 23.49/0.729 28.15/0.800 BESRNet (本文) × 4 29.18/0.860 26.10/0.742 25.74/0.714 23.81/0.751 28.23/0.813 Bicubic × 8 21.90/0.622 20.68/0.535 21.58/0.530 18.73/0.493 22.66/0.640 RDN[14] × 8 — — — — — RCAN[17] × 8 20.91/0.518 20.15/0.468 21.10/0.463 18.51/0.434 22.26/0.567 DRN[8] × 8 21.09/0.536 20.76/0.499 21.31/0.493 18.81/0.471 22.67/0.594 HAN[19] × 8 20.30/0.492 19.88/0.486 19.53/0.467 18.17/0.401 21.47/0.529 RDNMD × 8 23.86/0.710 21.79/0.560 22.70/0.569 20.29/0.586 24.18/0.686 ZSSR[30] × 8 — — — — — IKC[22] × 8 — — — — — BESRNet (本文) × 8 24.15/0.722 22.64/0.600 22.87/0.571 20.54/0.599 24.75/0.691 
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表 2 各个模糊核预测方法在基准数据集上的定量结果对比 (MSE × 10−5/MAE × 10−3)
Table 2 Quantitative comparison of kernel estimation methods on the benchmark datasets (MSE × 10−5/MAE × 10−3)
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表 3 (×4) 使用真值模糊核作为先验的不同方法的量化指标对比(PSNR (dB)/SSIM)
Table 3 (×4) Quantitative comparison of different methods with real blur kernels as prior (PSNR (dB)/SSIM)
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表 4 (×4) 不同DFS分支数的KAFS 模块在Set5[39]数据集上的定量结果对比
Table 4 (×4) Quantitative comparison of KAFS module with different numbers of DFS on Set5[39]
DFS PSNR (dB)/SSIM Params (M) Multi-adds (G) 1 29.50/0.861 12.92 151.04 2 29.61/0.863 12.98 151.05 4 29.54/0.862 13.12 151.06
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