Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
Source abstract
Abstract
Unsupervised low-light image enhancement methods have gained attention and shown improvement with low data dependence. However, the lack of a ground truth presents challenges, notably in pronounced noise and color bias. This paper proposes a Self-Guided Pixel-wise Calibration method to overcome associated issues by leveraging inherent features from the input as a self-guide. Specifically, a Pixel-wise Guided Filter is introduced to decrease noise, utilizing a low-light image for guidance and deep features as regularization maps. Additionally, a Color Correction Module is introduced to enhance saturation by adjusting the shadow threshold. Finally, a pixel-wise exposure control loss is formalized to optimize overall naturalness by adjusting brightness to a well-exposedness map from the low-light image. Extensive experiments demonstrate that our method outperforms many state-of-the-art methods, producing enhanced results with fewer distortions across various real-world image enhancement tasks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Enhancement Techniques · Advanced Image Processing Techniques · Advanced Vision and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Institute of USV Engineering, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
School of Future Technology, Shanghai University, Shanghai 200444, China
Funding
National Natural Science Foundation of China · Ministry of Science and Technology of the People’s Republic of China
References (45)
Show source-provided references
1. Zhang, N., Han, X., Liu, C., Gang, R., Ma, S., and Cao, Y. (2024). Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network. Appl. Sci., 14.10.3390/app14146320 ↗
2. Ji, S., Xu, S., Xiao, N., Cheng, X., Chen, Q., and Jiang, X. (2024). Boosting the Performance of LLIE Methods via Unsupervised Weight Map Generation Network. Appl. Sci., 14.10.3390/app14124962 ↗
3. Enhancing low-light images via dehazing principles: Essence and method10.1016/j.patrec.2024.07.017 ↗
4. Wang, M., Zhang, Z., and Liu, H. (2024). An Adaptive Semantic Segmentation Network for Adversarial Learning Domain Based on Low-Light Enhancement and Decoupled Generation. Appl. Sci., 14.10.3390/app14083295 ↗
5. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the ICLR 2015, San Diego, CA, USA.
6. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
7. An adaptive brightness preserving bi-histogram equalization
8. Image enhancement using background brightness preserving histogram equalisation10.1049/el.2011.3421 ↗
9. Contrast enhancement via texture region based histogram equalization10.1080/09500340.2016.1154194 ↗
10. LIME: Low-Light Image Enhancement via Illumination Map Estimation10.1109/TIP.2016.2639450 ↗
11. Fu, X., Zeng, D., Huang, Y., Zhang, X.P., and Ding, X. (2016, January 27–30). A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.304 ↗
12. Adaptive logarithmic mapping for displaying high contrast scenes10.1111/1467-8659.00689 ↗
13. Low illumination color image enhancement algorithm based on LIP model
14. Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution10.1109/TIP.2012.2226047 ↗
15. An enhancement algorithm for coal mine low illumination images based on bi-gamma function
16. Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., and Zhou, Y. (2020, January 6–10). Zero-Shot Restoration of Underexposed Images via Robust Retinex Decomposition. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.10.1109/ICME46284.2020.9102962 ↗
17. Fu, Z., Yang, Y., Tu, X., Huang, Y., Ding, X., and Ma, K.K. (2023, January 17–24). Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.10.1109/CVPR52729.2023.02131 ↗
18. Liu, R., Ma, L., Zhang, J., Fan, X., and Luo, Z. (2021, January 20–25). Retinex-Inspired Unrolling With Cooperative Prior Architecture Search for Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01042 ↗
19. Ma, L., Ma, T., Liu, R., Fan, X., and Luo, Z. (2022, January 18–24). Toward Fast, Flexible, and Robust Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.10.1109/CVPR52688.2022.00555 ↗
20. Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13–19). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00185 ↗
21. LLNet: A deep autoencoder approach to natural low-light image enhancement10.1016/j.patcog.2016.06.008 ↗
22. Pereira, F., Burges, C., Bottou, L., and Weinberger, K. Image Denoising and Inpainting with Deep Neural Networks. Proceedings of the Advances in Neural Information Processing Systems.
23. Zhu, M., Pan, P., Chen, W., and Yang, Y. (2020, January 7–12). EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network. Proceedings of the The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, USA.
24. EnlightenGAN: Deep Light Enhancement Without Paired Supervision10.1109/TIP.2021.3051462 ↗
25. (2006). Image Sensors and Signal Processing for Digital Still Cameras, CRC Press.
26. Chen, C., Chen, Q., Xu, J., and Koltun, V. (2018, January 18–23). Learning to See in the Dark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00347 ↗
27. Guided Image Filtering10.1109/TPAMI.2012.213 ↗
28. Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5–9 October 2015, Springer.10.1007/978-3-319-24571-3 ↗
29. WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification10.1109/TIP.2021.3101395 ↗
30. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images10.1109/TIP.2018.2794218 ↗
31. LECARM: Low-Light Image Enhancement Using the Camera Response Model10.1109/TCSVT.2018.2828141 ↗
32. Low-Light Image Enhancement with Semi-Decoupled Decomposition10.1109/TMM.2020.2969790 ↗
33. Low-light image enhancement using gamma correction prior in mixed color spaces10.1016/j.patcog.2023.110001 ↗
34. RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement10.1109/TCSVT.2021.3073371 ↗
35. Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv.
36. Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 13–19). From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00313 ↗
37. Perceptual Quality Assessment for Multi-Exposure Image Fusion10.1109/TIP.2015.2442920 ↗
38. Lee, C., Lee, C., and Kim, C.S. (2012–3, January 30). Contrast enhancement based on layered difference representation. Proceedings of the 2012 19th IEEE International Conference on Image Processing, Orlando, FL, USA.10.1109/ICIP.2012.6467022 ↗
39. Getting to know low-light images with the Exclusively Dark dataset10.1016/j.cviu.2018.10.010 ↗
40. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗
41. Making a “Completely Blind” Image Quality Analyzer10.1109/LSP.2012.2227726 ↗
42. No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics
43. Glenn Jocher, M.C. (2024, November 23). Yolov8. Available online: https://github.com/ultralytics/ultralytics.
44. Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Microsoft COCO: Common Objects in Context. Computer Vision—ECCV 2014, Proceedings of the 13th European Conference, Zurich, Switzerland, 6–12 September 2014, Springer.10.1007/978-3-319-10590-1 ↗
45. Pereira, F., Burges, C., Bottou, L., and Weinberger, K. ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems.