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DICNet: achieve low-light image enhancement with image decomposition, illumination enhancement, and color restoration

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Abstract

Low-light image enhancement (LLIE) is mainly used to restore image degradation caused by environmental noise, lighting effects, and other factors. Despite many relevant works combating environmental interference, LLIE currently still faces multiple limitations, such as noise, unnatural color recovery, and severe loss of details, etc. To effectively overcome these limitations, we propose a DICNet based on the Retinex theory. DICNet consists of three components: image decomposition, illumination enhancement, and color restoration. To avoid the influence of noise during the enhancement process, we use feature maps after the image high-frequency component denoising process to guide image decomposition and suppress noise interference. For illumination enhancement, we propose a feature separation method that considering the influence of different lighting intensities and preserves details. In addition, to address the insufficient high-low-level feature fusion of the U-Net used in color restoration, we design a Feature Cross-Fusion Module and propose a feature fusion connection plug-in to ensure natural and realistic color restoration. Based on a large number of experiments on publicly available datasets, our method outperforms existing state-of-the-art methods in both performance and visual quality.

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Data Availability

The LOL, LOL-v2-Real, and LSRW datasets used in this paper are publicly available. The LOL, LOL-v2-Real, and LSRW datasets can be acquired from the following links. All data used in this paper, including images and codes are available by contacting the corresponding author by reasonable request. LOL: https://pan.baidu.com/share/init?surl=ABMrDjBTeHIJGlOFIeP1IQ, LOL-v2-Real: https://pan.baidu.com/s/1U9ePTfeLlnEbr5dtI1tm5g, LSRW: https://pan.baidu.com/s/1XHWQAS0ZNrnCyZ-bq7MKvA.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Heilongjiang Province under Grant LH2022E024.

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Authors and Affiliations

  1. School of Electrical Engineering Information Department, Northeast Petroleum University, Daqing, 163318, China

    Heng Pan, Bingkun Gao, Xiufang Wang, Chunlei Jiang & Peng Chen

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  1. Heng Pan
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Contributions

Heng Pan: Conceptualization, Software, Methodology, Visualization, Validation, Writing-Original Draft, Resources. Bingkun Gao: Conceptualization, Project administration, Writing-Review & Editing, Supervision. Xiufang Wang: Funding Acquisition, Writing-Review & Editing, Supervision. Chunlei Jiang: Investigation, Resources, Writing-Original Draft. Peng Chen: Data Curation, Formal analysis.

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Correspondence to Bingkun Gao.

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Pan, H., Gao, B., Wang, X. et al. DICNet: achieve low-light image enhancement with image decomposition, illumination enhancement, and color restoration. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03262-0

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