Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
Source abstract
Abstract
Existing low-light image enhancement methods often struggle to effectively enhance space targets in deep-space contexts due to the effects of extremely low illumination, stellar stray light, and Earth halos. This work proposes a low-light image enhancement method based on multi-image fusion, which integrates features of space targets with the Retinex theory. The method dynamically adjusts contrast by detecting luminance distribution and incorporates an adaptive noise removal mechanism for enhanced image quality. This method effectively balances detail enhancement with noise suppression. This work presents experiments on deep-space background images featuring 10 types of artificial satellites, including AcrimSat, Calipso, Jason, and others. Experimental results demonstrate that the proposed method outperforms traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in suppressing Earth halo interference. This study establishes an effective framework for improving the visual quality of spacecraft images and provides important technical support for applications such as spacecraft identification, space target detection, and autonomous spacecraft navigation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Enhancement Techniques · Advanced Image Fusion Techniques · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
University of Chinese Academy of Sciences, Beijing 100049, China
Xi’an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi’an 710119, China
References (33)
Show source-provided references
1. Motion Prediction of a Non-Cooperative Space Target10.1016/j.asr.2017.10.028 ↗
2. Su, S., Niu, W., Li, Y., Ren, C., Peng, X., Zheng, W., and Yang, Z. (2023). Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning. Remote Sens., 15.10.3390/rs15092455 ↗
3. Bian, H., Cao, J., Zhang, G., Zhang, Z., Li, C., and Dong, J. (2024). TYCOS: A Specialized Dataset for Typical Components of Satellites. Appl. Sci., 14.10.3390/app14114757 ↗
4. Musallam, M.A., Gaudilliere, V., Ghorbel, E., Al Ismaeil, K., Perez, M.D., Poucet, M., and Aouada, D. (2021, January 19–22). Spacecraft Recognition Leveraging Knowledge of Space Environment: Simulator, Dataset, Competition Design and Analysis. Proceedings of the 2021 IEEE International Conference on Image Processing Challenges (ICIPC), Anchorage, AK, USA.10.1109/ICIPC53495.2021.9620184 ↗
5. Space Moving Target Detection and Tracking Method in Complex Background10.1016/j.infrared.2018.03.007 ↗
6. Dynamic Histogram Equalization for Contrast Enhancement for Digital Images10.1016/j.asoc.2020.106114 ↗
7. A Dynamic Histogram Equalization for Image Contrast Enhancement10.1109/TCE.2007.381734 ↗
8. Contextual and Variational Contrast Enhancement10.1109/TIP.2011.2157513 ↗
9. An Adaptive Gamma Correction for Image Enhancement10.1186/s13640-016-0138-1 ↗
10. Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction10.1109/ACCESS.2018.2797872 ↗
11. Retinex Processing for Automatic Image Enhancement10.1117/1.1636183 ↗
12. Parihar, A.S., and Singh, K. (2018, January 19–20). A Study on Retinex Based Method for Image Enhancement. Proceedings of the 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India.10.1109/ICISC.2018.8398874 ↗
13. Fast Algorithm of Image Enhancement Based on Multi-Scale Retinex10.1016/j.procs.2018.04.179 ↗
14. Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., and Zhang, Y. (2023, January 1–6). Retinexformer: One-Stage Retinex-Based Transformer for Low-Light Image Enhancement. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France.10.1109/ICCV51070.2023.01149 ↗
15. Yang, S., Ding, M., Wu, Y., Li, Z., and Zhang, J. (2023, January 1–6). Implicit Neural Representation for Cooperative Low-Light Image Enhancement. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France.10.1109/ICCV51070.2023.01187 ↗
16. Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep Retinex Decomposition for Low-Light Enhancement. arXiv.
17. 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, Las Vegas, NV, USA.10.1109/CVPR.2016.304 ↗
18. LIME: Low-Light Image Enhancement via Illumination Map Estimation10.1109/TIP.2016.2639450 ↗
19. Beyond Brightening Low-Light Images10.1007/s11263-020-01407-x ↗
20. Zhang, Y., Zhang, J., and Guo, X. (2019, January 15–19). Kindling the Darkness: A Practical Low-Light Image Enhancer. Proceedings of the 27th ACM International Conference on Multimedia, Amsterdam, The Netherlands.10.1145/3343031.3350926 ↗
21. Chen, C., Chen, Q., Do, M.N., and Koltun, V. (November, January 27). Seeing Motion in the Dark. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
22. An Improved Whale Optimization Algorithm Based on Multilevel Threshold Image Segmentation Using the Otsu Method10.1016/j.engappai.2022.104960 ↗
23. Implementing Canny Edge Detection Algorithm for Noisy Image10.11591/eei.v9i4.1837 ↗
24. Analysis of Image Processing Using Morphological Erosion and Dilation
25. Multimodal Medical Image Fusion Based on Joint Bilateral Filter and Local Gradient Energy10.1016/j.ins.2021.04.052 ↗
26. Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results10.1109/TAES.2020.2989063 ↗
27. Park, T.H., Märtens, M., Lecuyer, G., Izzo, D., and D’Amico, S. (2022, January 5–12). SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap. Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA.10.1109/AERO53065.2022.9843439 ↗
28. Park, T.H., and D’Amico, S. (2024, January 8–12). Rapid Abstraction of Spacecraft 3D Structure from Single 2D Image. Proceedings of the AIAA SCITECH 2024 Forum, Orlando, FL, USA.10.2514/6.2024-2768 ↗
29. Ahmed, Z., Park, T.H., Bhattacharjee, A., Razel-Rezai, R., Graves, R., Saarela, O., Teramoto, R., Vemulapalli, K., and D’Amico, S. (2024, January 2–7). SPEED-UE-Cube: A Machine Learning Dataset for Autonomous, Vision-Based Spacecraft Navigation. Proceedings of the 46th Rocky Mountain AAS Guidance, Navigation and Control Conference, Breckenridge, CO, USA.
30. Efficient Contrast Enhancement Using Adaptive Gamma Correction with Weighting Distribution10.1109/TIP.2012.2226047 ↗
31. R2rnet: Low-Light Image Enhancement via Real-Low to Real-Normal Network10.1016/j.jvcir.2022.103712 ↗
32. Image Quality Assessment: From Error Visibility to Structural Similarity10.1109/TIP.2003.819861 ↗
33. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18–22). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00068 ↗