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• W4387617891 startingPage "8442" @default.
• W4387617891 abstract "Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects." @default.
• W4387617891 created "2023-10-14" @default.
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• W4387617891 date "2023-10-13" @default.
• W4387617891 modified "2023-10-15" @default.
• W4387617891 title "Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory" @default.
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• W4387617891 doi "https://doi.org/10.3390/s23208442" @default.
• W4387617891 hasPublicationYear "2023" @default.
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