山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (6): 35-46.doi: 10.6040/j.issn.1672-3961.0.2022.298
王旭晴,魏伟波,杨光宇,宋金涛,吕婷,潘振宽*
WANG Xuqing, WEI Weibo, YANG Guangyu, SONG Jintao, LÜ Ting, PAN Zhenkuan*
摘要： 针对图像盲去模糊问题,基于变分模型的迭代优化展开形式设计了相应的变分深度学习网络,有效克服了传统变分方法计算效率低和深度学习方法可解释性差的问题。设计网络包含2部分: 利用算法展开策略实现基于L0正则化估计模糊核的子网络;基于估计的模糊核及图像恢复正则化模型的非盲去卷积子网络,该子网络充分利用了双通道的编解码网络结构。为确保模糊核估计的准确性和图像内容的一致性,损失函数由均方误差损失和结构相似性损失构成。L0正则化的使用有助于快速准确地完成模糊核估计;图像恢复正则化模型的使用有助于边缘和图像细节的保持。在Levin数据集上的试验结果表明,所提算法在峰值信噪比上较目前先进算法至少提高了2.14 dB。
中图分类号:
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