[6] [6] ABDULLAH-AL-WADUD M, KABIR M H, DEWAN M A A, et al. A dynamic histogram equalization for image contrast enhancement[J]. IEEE Transactions on Consumer Electronics, 2007, 53(2): 593-600.

[7] [7] LEE C, LEE C, KIM C S. Contrast enhancement based on layered difference representation of 2D histograms[J]. IEEE Transactions on Image Processing, 2013, 22(12): 5372-5384.

[8] [8] LAND E H. The retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108-129.

[9] [9] LAND E H, MCCANN J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1-11.

[10] [10] JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997, 6(7): 965-976.

[11] [11] RAHMAN Z, JOBSON D J, WOODELL G A. Multi-scale retinex for color image enhancement[C]//Proceedings of 3rd IEEE International Conference on Image Processing. New York, USA: IEEE Press, 1996: 1003-1006.

[12] [12] LORE K G, AKINTAYO A, SARKAR S. LLNet: A deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 2017, 61: 650-662.

[14] [14] JIANG Y, GONG X, LIU D, et al. EnlightenGan: Deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 2021, 30: 2340-2349.

[15] [15] LI C, GUO C, HAN L, et al. Low-light image and video enhancement using deep learning: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(12): 9396-9416.

[16] [16] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention 2015. Munich, Germany: Springer Press, 2015: 234-241.

[17] [17] ZHANG Z, LIU Q, WANG Y. Road extraction by deep residual u-net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753.

[18] [18] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2016: 770-778.

[19] [19] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern recognition. New York, USA: IEEE Press, 2018: 7132-7141.

[20] [20] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV). New York, USA: IEEE Press, 2018: 3-19.

[21] [21] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2021: 13713-13722.

[22] [22] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.

[23] [23] YU F, KOLTUN V, FUNKHOUSER T. Dilated residual networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2017: 472-480.

[24] [24] WANG P, CHEN P, YUAN Y, et al. Understanding convolution for semantic segmentation[C]//2018 IEEE Winter Conference on Applications of Computer Vision (WACV). New York, USA: IEEE Press, 2018: 1451-1460.

[25] [25] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[26] [26] ZHAO H, GALLO O, FROSIO I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2016, 3(1): 47-57.

[28] [28] KRUSE F A, LEFKOFF A B, BOARDMAN J W, et al. The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data[J]. Remote Sensing of Environment, 1993, 44(2-3): 145-163.

[30] [30] CHEN T, YANG W, ZHANG H, et al. Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data[J]. Computers and Electronics in Agriculture, 2020, 177: 105708.

[32] [32] WEI C, WANG W, YANG W, et al. Deep retinex decomposition for low-light enhancement[C]//British Machine Vision Conference 2018. New York, USA: IEEE Press, 2018: 3-6.

[33] [33] WANG W, WEI C, YANG W, et al. Gladnet: Low-light enhancement network with global awareness[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). New York, USA: IEEE Press, 2018: 751-755.

[34] [34] ZHANG Y, GUO X, MA J, et al. Beyond brightening low-light images[J]. International Journal of Computer Vision, 2021, 129: 1013-1037.