Abstract
In order to better extract image features and effectively remove noise features, we propose an image denoising model based on GRU (Gate Recurrent Unit) and Dense block. The GRU contacts the previous state through the current state, which can more effectively extract noise features from the picture. The Dense block can effectively improve the feature propagation efficiency, reduce the problem of vanishing gradient, and optimize the process of feature acquisition. The denoising performance can be effectively improved through the cascade of GRU and Dense block. Our extensive evaluations on two datasets demonstrate that the proposed model outperforms the state of the art methods under all different noise levels in terms of PNSR, and the visual effects achieved by the proposed model are also better than the competing methods.
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Acknowledgments
This work is supported by Natural Science Foundation of Guangdong Province of China under grant no. 2020A1515010784, the Guangdong Youth Characteristic Innovation Project under grant no. 2021KQNCX120.
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Zhu, F., Wang, Y., Tang, H. (2022). The Combination of GRU and Dense Block for Image Denoising Network. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_10
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DOI: https://doi.org/10.1007/978-981-19-4109-2_10
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