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A deep Retinex network for underwater low-light image enhancement

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Abstract

Underwater images suffer from color cast and low contrast due to the light absorption and scattering. Especially when natural light is not sufficient, large dark areas appear in the captured image, making it impossible to understand the image content. To address this issue, we propose an underwater low-light enhancement method based on Retinex theory. Our model is an end-to-end trainable. The decomposition network decomposes the raw image into reflectance and illumination according to Retinex theory. In the reflectance enhancement network, cross-residual blocks and dense connections can improve the efficiency of feature utilization and the hybrid attention concentrate on the regions of interest in feature maps from different perspectives. The illumination adjustment network utilizes adaptive frequency convolutional blocks to generate additional band information, which reconstructs the more natural illumination. In order to preserve the color consistency of the enhanced image with the reference image, we project the HSV space into the Cartesian coordinate system and use the Euclidean distance as the color cast loss to constrain the enhancement network. Qualitative and quantitative evaluations on different underwater datasets indicate that our method has the excellent performance and can achieve delightful visual enhancements.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities of China (No. N2216010), the ’Jie Bang Gua Shuai’ Science and Technology Major Project of Liaoning Province in 2022 (No. 2022JH1/10400025) and the National Key Research and Development Program of China (No. 2018YFB1702000).

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  1. Weimin Lei and Wei Zhang have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, Northeastern University, No.195, Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China

    Kai Ji, Weimin Lei & Wei Zhang

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  1. Kai Ji
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Kai Ji wrote the manuscript; Weimin Lei and Wei Zhang provided suggestions for the manuscript. All authors reviewed the manuscript.

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Correspondence to Weimin Lei.

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Ji, K., Lei, W. & Zhang, W. A deep Retinex network for underwater low-light image enhancement. Machine Vision and Applications 34, 122 (2023). https://doi.org/10.1007/s00138-023-01478-z

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