稳定增强生成对抗网络在壁画的超分辨率重建

doi:10.16182/j.issn1004731x.joss.20-0989

摘要/Abstract

摘要：

针对古代壁画分辨率低、纹理细节模糊不清导致壁画观赏性不足和研究价值不高的问题，提出了一种稳定增强生成对抗网络的超分辨率重建算法（stable enhanced super-resolution generative adversarial networks, SESRGAN）。以生成对抗网络为基础框架，生成网络采用密集残差块提取壁画特征，使用VGG（visual geometry group）网络作为判别网络的基本框架判断输入壁画的真假，引入感知损失、内容损失和惩罚损失三个损失共同优化模型。实验结果表明，与其他相关的超分辨率算法进行比较，峰值信噪比平均提高了0.4~2.62 dB，结构相似性提高了0.013~0.027，主观感知评估也有提高。

关键词: 古代壁画, 超分辨率重建, 生成对抗网络, 密集残差块, 惩罚损失

Abstract:

Aiming at the problems of low resolution and unclear texture details of ancient murals, which led to insufficient viewing of murals and low research value, a stable enhanced super-resolution generative adversarial networks (SESRGAN) reconstruction algorithm is proposed. Based on the generative adversarial network, the generative network uses dense residual blocks to extract mural features, and uses the visual geometry group (VGG) network as the basic framework of the discriminating network to determine the authenticity of the input mural, and introduces perception loss, content loss and penalty loss to jointly optimize the model. Experimental results show that, compared with other related super-resolution algorithms, the peak signal-to-noise ratio (PSNR) is improved by 0.4~2.62 dB on average, the structural similarity is improved by 0.013~0.027, and the subjective perception evaluation is also improved.

Key words: ancient mural, super-resolution reconstruction, generation adversarial network, dense residual block, penalty loss

中图分类号:

TP391.47

曹建芳, 贾一鸣, 闫敏敏, 田晓东. 稳定增强生成对抗网络在壁画的超分辨率重建[J]. 系统仿真学报, 2022, 34(5): 1076-1089.

Jianfang Cao, Yiming Jia, Minmin Yan, Xiaodong Tian. Murals Super-resolution Reconstruction with the Stable Enhanced Generative Adversarial Network[J]. Journal of System Simulation, 2022, 34(5): 1076-1089.

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Name	Type	Kernel	Stride	Padding	Output
CONV0_0	CONV	3×3	1×1	1	64
CONV1_0	CONV	4×4	2×2	1	64
CONV1_1	CONV	3×3	1×1	1	64
CONV2_0	CONV	4×4	2×2	1	128
CONV2_1	CONV	3×3	1×1	1	128
CONV3_0	CONV	4×4	2×2	1	256
CONV3_1	CONV	3×3	1×1	1	256
CONV4_0	CONV	4×4	2×2	1	512
CONV4_1	CONV	3×3	1×1	1	512
FC0	FC	-	-	-	100
FC1	FC	-	-	-	1

图像	PSNR	SSIM
a	28.71	0.876
b	32.03	0.782
c	32.03	0.782
d	32.03	0.782

图像	BI	文献[4]	文献[5]	SRGAN	ESRGAN	ESERGAN
a	30.58/0.827	30.78/0.824	30.97/0.831	31.24/0.843	32.06/0.862	32.53/0.859
b	28.01/0.792	28.24/0.799	28.16/0.795	28.39/0.806	28.60/0.809	29.20/0.824
c	22.69/0.741	23.96/0.743	24.84/0.745	25.17/0.749	26.20/0.751	27.03/0.776
d	25.48/0.739	26.54/0.740	27.29/0.751	26.59/0.743	28.42/0.746	28.06/0.764
e	24.41/0.764	25.16/0.766	25.75/0.771	25.81/0.767	26.54/0.774	27.29/0.786
f	23.59/0.731	24.88/0.730	25.43/0.733	25.66/0.734	26.28/0.738	26.39/0.743
Avg	25.79/0.765	26.59/0.767	27.07/0.771	27.14/0.773	28.01/0.779	28.41/0.792

图像	含有BN层	含有SN层
a	7.31	9.54
b	6.47	8.25
c	7.06	7.03
d	5.36	6.49
e	5.45	7.24
f	6.17	8.49