1. Ye Y S, Sun Q, Shi L, et al. A adaptive dual-platform deep space infrared image enhancement algorithm based on linear gray scale transformation. Proceedings of the 33rd Chinese Control Conference (CCC’14), Jul 28-30, 2014, Nanjing, China. Piscataway, NJ, USA: IEEE, 2014: 7434-7438 2. Yao Z J, Lai Z Y, Wang C, et al. Brightness preserving and contrast limited bi-histogram equalization for image enhancement. Proceedings of the 3rd International Conference on Systems and Informatics (ICSAI’16), Nov 19-21, 2016, Shanghai, China. Piscataway, NJ, USA: IEEE, 2016: 866-870 3. Park S, Moon B, Ko S Y, et al. Low-light image enhancement using variational optimization-based Retinex model. Proceedings of the 2017 IEEE International Conference on Consumer Electronics (ICCE’17), Jan 8-10, 2017, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2017: 70-71 4. Xu X J, Wang Y R, Yang G S, et al. Image enhancement method based on fractional wavelet transform. Proceedings of the 2016 IEEE International Conference on Signal and Image Processing (ICSIP’16), Aug 13-15, 2016, Beijing, China. Piscataway, NJ, USA: IEEE, 2016: 194-197 5. Zhou F, Ma X L, Li Y, et al. Medical image enhancement based on NSCT. Proceedings of the 2013 International Conference on Smart and Sustainable City (ICSSC’13), Aug 19-20, 2013, Shanghai, China. Piscataway, NJ, USA: IEEE, 2013: 166-169 6. Li L L, Si Y J. Remote sensing image enhancement based on nonsubsampled shearlet transform and local laplacian filter. Proceedings of the 3rd International Conference on Image, Vision and Computing (ICIVC’18), Jun 27-29, 2018, Chongqing, China. Piscataway, NJ, USA: IEEE, 2018: 415-418 7. Easley G R, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46 8. Easley G R, Labate D, Lim W Q. Optimally sparse image representations using shearlets. Proceedings of the 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, Oct 29-Nov 1, 2006, Pacific Grove, CA, USA. Piscataway, NJ, USA: IEEE, 2006: 974-978 9. Hanmandlu M, Verma O P, Kumar N K, et al. A novel optimal fuzzy system for color image enhancement using bacterial foraging. IEEE Transactions on Instrumentation and Measurement, 2009, 58(8): 2867-2879 10. He K M, Sun J, Tang X. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409 11. Li Z G, Zheng J H, Zhu Z J, et al. Weighted guided image filtering. IEEE Transactions on Image Processing, 2015, 24(1): 120-129 12. Kou F, Chen W H, Wen C Y, et al. Gradient domain guided image filtering. IEEE Transactions on Image Processing, 2015, 24(11): 4528-4539 13. Lu Z W, Long B Y, Li K, et al. Effective guided image filtering for contrast enhancement. IEEE Signal Processing Letters, 2018, 25(10): 1585-1589 14. Guo X J, Li Y, Ling H B. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 2017, 26(2): 982-993 15. Li M D, Liu J Y, Yang W H, et al. Structure-revealing low-light image enhancement via robust retinex model. IEEE Transactions on Image Processing, 2018, 27(6): 2828-2841 16. Ren X T, Cheng W H, Liu J Y, et al. Joint enhancement and denoising method via sequential decomposition. Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS’18), May 27-30, 2018, Florence, Italy. Piscataway, NJ, USA: IEEE, 2018: 5p 17. Ying Z Q, Li G, Ren Y R, et al. A new low-light image enhancement algorithm using camera response model. Proceedings of 2017 IEEE International Conference on Computer Vision Workshops (ICCVW’17), Oct 22-29, 2017, Venice, Italy. Piscataway, NJ, USA: IEEE, 2017: 3015-3022 |