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The Journal of Supercomputing
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21-04-2024

FCNet: a deep neural network based on multi-channel feature cascading for image denoising

Authors: Siling Feng, Zhisheng Qi, Guirong Zhang, Cong Lin, Mengxing Huang

Published in: The Journal of Supercomputing | Issue 12/2024

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Abstract

The article introduces FCNet, a deep neural network designed for image denoising that leverages multi-channel feature cascading to enhance performance and convergence speed. The network consists of five key components: Sparse Block, Feature Fusion Block, Feature Compression Block, Information Interaction Block, and Reconstruction Block. Each component plays a crucial role in capturing and processing noise features at various levels. The FCNet architecture is evaluated against state-of-the-art methods and demonstrates superior denoising capabilities, particularly in handling both known and blind denoising tasks. The authors highlight the importance of channel features and propose innovative techniques to reuse underlying features, leading to faster convergence and improved denoising performance. The article also discusses the use of L1 loss to enhance robustness and the effectiveness of the Squeeze-and-Excitation module in improving denoising performance. Comprehensive experimental results on various datasets showcase the superiority of FCNet in both quantitative and qualitative evaluations. The article concludes with a discussion on future research directions, including the removal of real noise and the interpretability of denoising models.
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Metadata
Title
FCNet: a deep neural network based on multi-channel feature cascading for image denoising
Authors
Siling Feng
Zhisheng Qi
Guirong Zhang
Cong Lin
Mengxing Huang
Publication date
21-04-2024
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 12/2024
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-024-06045-5

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