Abstract
In recent years, deep convolutional neural networks have shown good performance on images with spatially invariant noise, but their performance is limited on real-world noisy images. In order to improve the practicality of denoising algorithms, this paper proposes a simple one-stage blind real image denoising network with a modular structure, which combines the local modeling capability of residual dense convolutional layers with the global modeling capability of spatial and channel attention blocks and inserts them as the main building blocks into widely used encoder–decoder architectures to achieve end-to-end denoising. To preserve image structure information as much as possible during the denoising process, this paper uses different lengths of residuals between different modules to mitigate the flow of low-frequency information, and applies Contrast-Aware channel attention to enhance the dependency relationship of channel activation. Furthermore, this paper evaluates state-of-the-art algorithms on different noise datasets using quantitative metrics and visual quality, experimental results demonstrate the superiority of the proposed algorithm.
Supported by the National Natural Science Foundation of China (62277001), National Natural Science Foundation of China (62201018), R &D Program of Beijing Municipal Education Commission (KM202310011013) and Beijing Natural Science Foundation (4222003).
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Cai, Q., Cao, Y., Wang, C., Li, H., Ma, M. (2023). CARDNet: A Denoiser Based on Contrast-Aware and Residual-Dense Block. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_69
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