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Unsupervised Low-Light Image Enhancement via Spectral Consistency

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15322))

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Abstract

Retinex-based unsupervised low-light enhancement methods have demonstrated notable performance without paired data. However, existing Retinex-based unsupervised methods implicitly relax the constraints of Retinex theory and cannot predict the illumination and reflectance exactly, resulting in unstable outcomes. In order to alleviate this issue, we propose a novel framework with stringent consistent constraints for robust Retinex decomposition. Our work is inspired by the spectral characteristics of the low-light images and primarily utilizes the spectral perturbations to establish the training constraints. Specifically, we first investigate the invariant and equivariant components for low-light enhancement under spectral perturbations. Based on these consistency attributes, we design an illumination invariance constraint and a reflectance equivariance constraint for robust decomposition. Furthermore, motivated by the noise distribution under spectral perturbations, we introduce a cross multi-scale noise regularization technique to tackle the severe noise on the reflectance maps. Extensive experiments conducted on diverse datasets have demonstrated the superior performance over state-of-the-art approaches, highlighting its effectiveness and potential for various applications.

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Notes

  1. 1.

    https://github.com/lbu19/SCLLIE/blob/main/supplementary.pdf

References

  1. Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 18(9), 1921–1935 (2009)

    Article  MathSciNet  Google Scholar 

  2. Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., Zhang, Y.: Retinexformer: One-stage retinex-based transformer for low-light image enhancement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12504–12513 (2023)

    Google Scholar 

  3. Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)

    Google Scholar 

  4. Du, W., Chen, H., Yang, H.: Learning invariant representation for unsupervised image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14483–14492 (2020)

    Google Scholar 

  5. Fei, B., Lyu, Z., Pan, L., Zhang, J., Yang, W., Luo, T., Zhang, B., Dai, B.: Generative diffusion prior for unified image restoration and enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9935–9946 (2023)

    Google Scholar 

  6. Fu, H., Zheng, W., Meng, X., Wang, X., Wang, C., Ma, H.: You do not need additional priors or regularizers in retinex-based low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18125–18134 (2023)

    Google Scholar 

  7. Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2782–2790 (2016)

    Google Scholar 

  8. Fu, Z., Yang, Y., Tu, X., Huang, Y., Ding, X., Ma, K.K.: Learning a simple low-light image enhancer from paired low-light instances. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22252–22261 (2023)

    Google Scholar 

  9. Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1780–1789 (2020)

    Google Scholar 

  10. Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)

    Article  MathSciNet  Google Scholar 

  11. Hai, J., Xuan, Z., Yang, R., Hao, Y., Zou, F., Lin, F., Han, S.: R2RNet: Low-light image enhancement via real-low to real-normal network. Journal of Visual Communication and Image Representation 90, 103712.1–12 (2023)

    Google Scholar 

  12. Han, J., Ding, J., Xue, N., Xia, G.S.: Redet: A rotation-equivariant detector for aerial object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2786–2795 (2021)

    Google Scholar 

  13. Hao, S., Han, X., Guo, Y., Xu, X., Wang, M.: Low-light image enhancement with semi-decoupled decomposition. IEEE Trans. Multimedia 22(12), 3025–3038 (2020)

    Article  Google Scholar 

  14. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9729–9738 (2020)

    Google Scholar 

  15. Huang, T., Li, S., Jia, X., Lu, H., Liu, J.: Neighbor2neighbor: Self-supervised denoising from single noisy images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14781–14790 (2021)

    Google Scholar 

  16. Ibrahim, H., Kong, N.S.P.: Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(4), 1752–1758 (2007)

    Article  Google Scholar 

  17. Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., Wang, Z.: Enlightengan: Deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340–2349 (2021)

    Article  Google Scholar 

  18. Jobson, D.J., Rahman, Z.u., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing 6(7), 965–976 (1997)

    Google Scholar 

  19. Ke, R., Schönlieb, C.B.: Unsupervised image restoration using partially linear denoisers. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5796–5812 (2021)

    Google Scholar 

  20. Land, E.H.: The retinex theory of color vision. Sci. Am. 237(6), 108–129 (1977)

    Article  Google Scholar 

  21. Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation. In: Proceedings of the 19th IEEE International Conference on Image Processing. pp. 965–968 (2012)

    Google Scholar 

  22. Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans. Image Process. 22(12), 5372–5384 (2013)

    Article  Google Scholar 

  23. Lee, J., Kim, B., Kim, S., Cho, M.: Learning rotation-equivariant features for visual correspondence. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21887–21897 (2023)

    Google Scholar 

  24. Lenc, K., Vedaldi, A.: Understanding image representations by measuring their equivariance and equivalence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 991–999 (2015)

    Google Scholar 

  25. Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.M., Gu, J., Loy, C.C.: Low-light image and video enhancement using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 9396–9416 (2021)

    Article  Google Scholar 

  26. Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828–2841 (2018)

    Article  MathSciNet  Google Scholar 

  27. Lin, X., Ren, C., Liu, X., Huang, J., Lei, Y.: Unsupervised image denoising in real-world scenarios via self-collaboration parallel generative adversarial branches. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12642–12652 (2023)

    Google Scholar 

  28. Lin, X., Yue, J., Ding, S., Ren, C., Guo, C.L., Li, C.: Unlocking low-light-rainy image restoration by pairwise degradation feature vector guidance. arXiv preprint arXiv:2305.03997 (2023)

  29. Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10561–10570 (2021)

    Google Scholar 

  30. Liu, S., Zhang, Y.: Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans. Consum. Electron. 65(3), 303–311 (2019)

    Article  Google Scholar 

  31. Lore, K.G., Akintayo, A., Sarkar, S.: LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. 61, 650–662 (2017)

    Article  Google Scholar 

  32. Ma, K., Zeng, K., Wang, Z.: Perceptual quality assessment for multi-exposure image fusion. IEEE Trans. Image Process. 24(11), 3345–3356 (2015)

    Article  MathSciNet  Google Scholar 

  33. Ma, L., Ma, T., Liu, R., Fan, X., Luo, Z.: Toward fast, flexible, and robust low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5637–5646 (2022)

    Google Scholar 

  34. Park, G.H., Cho, H.H., Choi, M.R.: A contrast enhancement method using dynamic range separate histogram equalization. IEEE Trans. Consum. Electron. 54(4), 1981–1987 (2008)

    Article  Google Scholar 

  35. Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6849–6857 (2019)

    Google Scholar 

  36. Wang, S., Zheng, J., Hu, H.M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)

    Article  Google Scholar 

  37. Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 (2018)

  38. Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., Jiang, J.: Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5901–5910 (2022)

    Google Scholar 

  39. Xu, J., Hou, Y., Ren, D., Liu, L., Zhu, F., Yu, M., Wang, H., Shao, L.: Star: A structure and texture aware retinex model. IEEE Trans. Image Process. 29, 5022–5037 (2020)

    Article  Google Scholar 

  40. Xu, X., Wang, R., Fu, C.W., Jia, J.: SNR-aware low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17714–17724 (2022)

    Google Scholar 

  41. Yang, W., Wang, S., Fang, Y., Wang, Y., Liu, J.: From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3063–3072 (2020)

    Google Scholar 

  42. Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: A practical low-light image enhancer. In: Proceedings of the 27th ACM International Conference on Multimedia. pp. 1632–1640 (2019)

    Google Scholar 

  43. Zhang, Y., Di, X., Zhang, B., Wang, C.: Self-supervised image enhancement network: Training with low light images only. arXiv preprint arXiv:2002.11300 (2020)

  44. Zhao, L., Lu, S.P., Chen, T., Yang, Z., Shamir, A.: Deep symmetric network for underexposed image enhancement with recurrent attentional learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12075–12084 (2021)

    Google Scholar 

  45. Zhong, Y., Bhattad, A., Wang, Y.X., Forsyth, D.: Improving equivariance in state-of-the-art supervised depth and normal predictors. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 21775–21785 (2023)

    Google Scholar 

  46. Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., Zhou, Y.: Zero-shot restoration of underexposed images via robust retinex decomposition. In: Proceedings of the IEEE International Conference on Multimedia and Expo. pp. 1–6 (2020)

    Google Scholar 

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Acknowledgments

This work was supported by the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, 230027, China

    Bing Li, Wei Yu, Naishan Zheng, Jie Huang & Feng Zhao

Authors
  1. Bing Li
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  2. Wei Yu
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  3. Naishan Zheng
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  4. Jie Huang
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  5. Feng Zhao
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Corresponding author

Correspondence to Feng Zhao .

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Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Umapada Pal

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Li, B., Yu, W., Zheng, N., Huang, J., Zhao, F. (2025). Unsupervised Low-Light Image Enhancement via Spectral Consistency. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15322. Springer, Cham. https://doi.org/10.1007/978-3-031-78312-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-78312-8_14

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