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摘要: 深度卷积神经网络在单图像超分辨率重建方面取得了卓越成就，但其良好表现通常以巨大的参数数量为代价.本文提出一种简洁紧凑型递归残差网络结构，该网络通过局部残差学习减轻训练深层网络的困难，引入递归结构保证增加深度的同时控制模型参数数量，采用可调梯度裁剪方法防止产生梯度消失/梯度爆炸，使用反卷积层在网络末端直接上采样图像到超分辨率输出图像.基准测试表明，本文在重建出同等质量超分辨率图像的前提下，参数数量及计算复杂度分别仅为VDSR方法的1/10和1/（2n2）.Abstract: Despite the great success in single image super-resolution reconstruction achieved by deep convolutional neural network, the number of the computational parameters is often very large. This paper proposes a concise and compact recursive residual network. The local residual learning method is adopted to mitigate the difficulty of training very deep network, the recursive structure is configured to control the number of model parameters while increasing the model depth, the adjustable gradient clipping strategy is applied to prevent the gradient disappearance/gradient explosion, and a deconvolutional layer is set to directly up sample the image to a super-resolution image at the end of the residual network. According to benchmark tests, in the premise that the same quality super-resolution image is reconstructed, the number of parameters and the computational complexity of the proposed method are reduced to about 1/10 and 1/(2n2) of VDSR, respectively.1) 本文责任编委 王亮
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图 5 各种U、C组合所构成网络的性能对比图
Fig. 5 The performance of various networks at U and C combinations
图 6 各种SISR方法的×3模型在Set5测试集上的平均PSNR值及参数数量
Fig. 6 Average PSNR and number of parameters on the testset Set5 for scale factor ×3 of various SISR methods
图 7 测试集BSD100中的"img_092"重建结果对比图
Fig. 7 A comparison of the reconstruction results of "img_092" in the testset BSD100
图 8 测试集Set5中的"butterfly"重建对比图
Fig. 8 A comparison of the reconstruction results of "butterfly" in the testset Set5
表 1 不同RRSR组件构成的$\times3$模型在Set5测试集上的平均PSNR值及参数量
Table 1 Average PSNR and number of parameters when different RRSR components are turned on or off, for scale factor $\times3$ on testset Set5
局部残差 递归结构 PSNR (dB) 参数数量($\times10^3$) × × 33.27 371 √ × 33.58 371 √ √ 33.70 39 
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表 2 各种SISR方法的$\times2$, $\times3$和$\times4$模型在测试集Set5、Set14和BSD100上的平均PSNR值与SSIM值
Table 2 Average PSNR/SSIMs of various SISR methords for scale factor $\times2$, $\times3$ and $\times4$ on Set5, Set14 and BSD100
测试集 放大倍数 Bicubic PSNR/SSIM SelfEx PSNR/SSIM SRCNN PSNR/SSIM FSRCNN PSNR/SSIM VDSR PSNR/SSIM RRSR PSNR/SSIM Set5 ×2 33.66/0.9299 36.49/0.9537 36.66/0.9542 37.00/0.9558 37.53/0.9587 37.55/0.9588 Set5 ×3 30.39/0.8682 32.58/0.9093 32.75/0.9090 33.16/0.9140 33.66/0.9213 33.70/0.9208 Set5 ×4 28.42/0.8104 30.31/0.8619 30.48/0.8628 30.71/0.8657 31.35/0.8838 31.32/0.8836 Set14 ×2 30.24/0.8688 32.22/0.9034 32.42/0.9063 32.64/0.9088 33.03/0.9124 33.04/0.9125 Set14 ×3 27.55/0.7742 29.16/0.8196 29.28/0.8209 29.43/0.8242 29.77/0.8314 29.75/0.8307 Set14 ×4 26.00/0.7027 27.40/0.7518 27.49/0.7503 27.60/0.7535 28.01/0.7674 28.00/0.7675 BSD100 ×2 29.56/0.8431 31.18/0.8855 31.36/0.8879 31.51/0.8906 31.90/0.8960 31.91/0.8961 BSD100 ×3 27.21/0.7385 28.29/0.7840 28.41/0.7863 28.52/0.7897 28.82/0.7976 28.78/0.7969 BSD100 ×4 25.96/0.6675 26.84/0.7106 26.90/0.7101 26.97/0.7128 27.29/0.7251 27.25/0.7249
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