吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (10): 2923-2931.doi: 10.13229/j.cnki.jdxbgxb.20211299
多尺度生成对抗网络下图像压缩感知重建算法
曾春艳1(
),严康1,王志锋2(),王正辉1
- 1.湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室，武汉 430068
2.华中师范大学 教育信息技术学院，武汉 430079
Multi-scale generative adversarial network for image compressed sensing and reconstruction algorithm
Chun-yan ZENG1(),Kang YAN1,Zhi-feng WANG2(),Zheng-hui WANG1
- 1.Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System，Hubei University of Technology，Wuhan 430068，China
2.School of Education Information Technology，Central China Normal University，Wuhan 430079，China
摘要：
针对目前基于深度学习的压缩感知重建网络存在单通道重建网络没有深入挖掘图像的多尺度特征，缺乏对重建网络的反馈机制，并且重建网络缺乏与测量矩阵的关联，制约了重建质量的进一步提升的问题，提出了一种多尺度生成对抗网络下图像压缩感知与重建算法。该算法先通过多通道残差块提取图像的多尺度信息，加入判别网络形成对多尺度生成网络的反馈，再将全卷积测量网络与重建网络联合训练，以提升图像重建质量。实验结果表明：本文方法相对于ISTA-Net+方法在3种采样率下重建精度提高了2.02~4.09 dB。
中图分类号:
- TN911.73
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