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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.


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, 1998, -1(-1): .

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author="Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU",
journal="Frontiers of Information Technology & Electronic Engineering",
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000353

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A1 - Dan ZHANG
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A1 - Duanqing XU
A1 - Dongming LU
J0 - Journal of Zhejiang University Science C
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Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000353

deep learning has proven to be an effective mechanism for computer vision tasks, specifically, 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 combining 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 increase 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. Experiments on synthetic and real noisy images show that our algorithm is competitive with other algorithms.

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