Full Text:   <1964>

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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: 3191

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Dan ZHANG

https://orcid.org/0000-0002-5033-8128

Lei ZHAO

https://orcid.org/0000-0003-4791-454X

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.220-233

http://doi.org/10.1631/FITEE.2000353


Dual-constraint burst image denoising method


Author(s):  Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU

Affiliation(s):  Network and Media Laboratory, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   cszhd@zju.edu.cn, cszhl@zju.edu.cn, xdq@zju.edu.cn, ldm@zju.edu.cn

Key Words:  Image denoising, Burst image denoising, Deep learning


Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU. Dual-constraint burst image denoising method[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 220-233.

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author="Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU",
journal="Frontiers of Information Technology & Electronic Engineering",
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pages="220-233",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000353"
}

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%A Dongming LU
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A1 - Dan ZHANG
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Abstract: 
deep learning has proven to be an effective mechanism for computer vision tasks, especially for image denoising and burst image denoising. In this paper, we focus on solving the burst image denoising problem and aim to generate a single clean image from a burst of noisy images. We propose to combine the power of block matching and 3D filtering (BM3D) and a convolutional neural network (CNN) for burst image denoising. In particular, we design a CNN with a divide-and-conquer strategy. First, we employ BM3D to preprocess the noisy burst images. Then, the preprocessed images and noisy images are fed separately into two parallel CNN branches. The two branches produce somewhat different results. Finally, we use a light CNN block to combine the two outputs. In addition, we improve the performance by optimizing the two branches using two different constraints: a signal constraint and a noise constraint. One maps a clean signal, and the other maps the noise distribution. In addition, we adopt block matching in the network to avoid frame misalignment. Experimental results on synthetic and real noisy images show that our algorithm is competitive with other algorithms.

基于双重约束的多帧图像降噪方法

张丹,赵磊,许端清,鲁东明
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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