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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Generative Adaptive Convolutions for Real-World Noisy Image Denoising

February 1, 2023

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Authors

Ruijun Ma

University of Macau Guangdong Polytechnic Normal University


Shuyi Li

University of Macau


Bob Zhang

Univerisity of Macau


Zhengming Li

Guangdong Polytechnic Normal University


DOI:

10.1609/aaai.v36i2.20088


Abstract:

Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with flexibility that can adapt to the specific input and thereby impedes the FADNet from overfitting to the training data. Specifically, we exploit the advantage of the spatial and channel attention, and utilize this to devise a decoupling filter generation scheme. The generated filters are conditioned on the input and collaboratively applied to the decoded features for representation capability enhancement. We additionally introduce the Fourier transform and its inverse to guide the predicted weight filters to adapt to the noisy input with respect to the image contents. Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage.

Topics: AAAI

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HOW TO CITE:

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li Generative Adaptive Convolutions for Real-World Noisy Image Denoising Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li Generative Adaptive Convolutions for Real-World Noisy Image Denoising AAAI 2022, 1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li (2022). Generative Adaptive Convolutions for Real-World Noisy Image Denoising. Proceedings of the AAAI Conference on Artificial Intelligence, 1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. Generative Adaptive Convolutions for Real-World Noisy Image Denoising. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. 2022. Generative Adaptive Convolutions for Real-World Noisy Image Denoising. "Proceedings of the AAAI Conference on Artificial Intelligence". 1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. (2022) "Generative Adaptive Convolutions for Real-World Noisy Image Denoising", Proceedings of the AAAI Conference on Artificial Intelligence, p.1935-1943

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li, "Generative Adaptive Convolutions for Real-World Noisy Image Denoising", AAAI, p.1935-1943, 2022.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. "Generative Adaptive Convolutions for Real-World Noisy Image Denoising". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. "Generative Adaptive Convolutions for Real-World Noisy Image Denoising". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1935-1943.

Ruijun Ma||Shuyi Li||Bob Zhang||Zhengming Li. Generative Adaptive Convolutions for Real-World Noisy Image Denoising. AAAI[Internet]. 2022[cited 2023]; 1935-1943.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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