CLC number: TP391
On-line Access: 2022-02-28
Received: 2020-07-17
Revision Accepted: 2022-04-22
Crosschecked: 2021-03-28
Cited: 0
Clicked: 5230
Citations: Bibtex RefMan EndNote GB/T7714
Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU. Dual-constraint burst image denoising method[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000353 @article{title="Dual-constraint burst image denoising method", %0 Journal Article TY - JOUR
基于双重约束的多帧图像降噪方法浙江大学计算机科学与技术学院网络与媒体实验室,中国杭州市,310027 摘要:深度学习在计算机视觉领域应用非常成功,促进了图像降噪和多帧图像降噪领域的快速发展。本文针对多帧图像降噪问题,提出一种从多帧噪声图像中恢复清晰图像的方法。该方法结合BM3D(块匹配和三维滤波,block-matching and 3D filtering)算法和卷积神经网络(CNN)模型完成多帧图像降噪任务。该CNN模型基于分治法的思想设计。首先,用BM3D算法处理带噪声的多帧图像。然后,将预处理后的图像和原始噪声图像分别输入CNN模型的两个并行分支。最后,用一个轻量级CNN模块融合两个分支的输出得到最终图像估计。与以往研究不同,我们对CNN中两个并行分支分配了不同约束函数--信号约束和噪声约束,以提升模型提取不同特征的能力。此外,引入图像块匹配策略解决帧不对齐问题。在合成和真实噪声图像上的实验结果表明,该算法与其他算法相比具有一定竞争力。关键词:图像降噪;多帧图像降噪;深度学习 Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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