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Ambient Illumination Disentangled Based Weakly-Supervised Image Restoration Using Adaptive Pixel Retention Factor

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15038))

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Abstract

Existing image restoration algorithms are typically designed for specific domains. It is extremely challenging to achieve enhancement for both low-light and underwater images through a single model. Additionally, due to the extremely limited number of annotated underwater images, the model’s generalization performance is poor. In this paper, we present an ambient illumination disentangled network based weakly-supervised image restoration (WSIR) approach, aiming to utilize incomplete labeled images to achieve the restoration of various low-quality images. On the one hand, we design an illumination disentanglement network (Idnet) to learn the mapping rules for Retinex theory, and establish a data-driven camera response function (DdCRF) for illumination adjustment. On the other hand, we design a Adaptive Pixel Retention Factor Network (APRFNet) for generating the parameter maps in DdCRF, that improves its robustness and flexibility in complex and changeable environments, promoting the authenticity and visual aesthetics of the reconstructed results. Extensive experiments on public datasets and self-collected images demonstrate that our proposed scheme outperforms state-of-the-art methods in both qualitative and quantitative metrics.

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant U22A2066, Grant 61733014 and Grant U21B2047.

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Notes

  1. 1.

    https://sites.google.com/site/vonikakis/datasets.

  2. 2.

    https://aistudio.baidu.com/datasetdetail/228251.

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, West Youyi Road, Xi’an, 710129, China

    Ruiqi Mao & Rongxin Cui

Authors
  1. Ruiqi Mao
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  2. Rongxin Cui
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Corresponding author

Correspondence to Rongxin Cui .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, Xinjiang, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Mao, R., Cui, R. (2025). Ambient Illumination Disentangled Based Weakly-Supervised Image Restoration Using Adaptive Pixel Retention Factor. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15038. Springer, Singapore. https://doi.org/10.1007/978-981-97-8685-5_13

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  • DOI: https://doi.org/10.1007/978-981-97-8685-5_13

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