[1] 康晓东, 王昊, 郭军, 等.无监督深度学习彩色图像识别方法[J]. 计算机应用, 2015, 35(9):2636-2639. (TANG X D, WANG H, GUO J, et al. Unsupervised deep learning method for color image recognition[J]. Journal of Computer Applications, 2015, 35(9):2636-2639.) [2] 杨朔, 陈丽芳, 石瑀, 等.基于深度生成式对抗网络的蓝藻语义分割[J]. 计算机应用, 2018, 38(6):1554-1561. (YANG S, CHEN L F, SHI Y, et al. Semantic segmentation of blue-green algae based on deep generative adversarial net[J]. Journal of Computer Applications, 2018, 38(6):1554-1561.) [3] LI H M. Deep learning for image denoising[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3):171-180. [4] CAI B, XU X, JIA K, et al. DehazeNet:an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11):5187-5198. [5] REN W, LIU S, ZHANG H, et al. Single image dehazing via multi-scale convolutional neural networks[C]//ECCV 2016:Proceedings of the 2016 European Conference on Computer Vision. Berlin:Springer, 2016:154-169. [6] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ:IEEE, 2017:136-144. [7] LAND E H. The Retinex[J]. American Scientist, 1964, 52(2):247-264. [8] 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. [9] PISANO E D, ZONG S, HEMMINGER B M, et al. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms[J]. Journal of Digital Imaging, 1998, 11(4):193-200. [10] GUO X, LI Y, LING H. LIME:low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing, 2017, 26(2):982-993. [11] FU X, LIAO Y, ZENG D, et al. A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation[J]. IEEE Transactions on Image Processing, 2015, 24(12):4965-4977. [12] FU X, ZENG D, HUANG Y, et al. A weighted variational model for simultaneous reflectance and illumination estimation[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:2782-2790. [13] WANG S, ZHENG J, HU H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing, 2013, 22(9):3538-3548. [14] FU X, ZENG D, HUANG Y, et al. A fusion-based enhancing method for weakly illuminated images[J]. Signal Processing, 2016, 129:82-96. [15] DONG X, WANG G, PANG Y, et al. Fast efficient algorithm for enhancement of low lighting video[C]//Proceedings of the 2011 IEEE International Conference on Multimedia and Expo. Piscataway, NJ:IEEE, 2011:1-6. [16] FOTIADOU K, TSAGKATAKIS G, TSAKALIDES P. Low light image enhancement via sparse representations[C]//ICIAR 2014:Proceedings of the 2014 International Conference on Image Analysis and Recognition. Berlin:Springer, 2014:84-93. [17] LI C, GUO J, PORIKLI F, et al. LightenNet:a convolutional neural network for weakly illuminated image enhancement[J]. Pattern Recognition Letters, 2018, 104:15-22. [18] 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. [19] MAO X J, SHEN C, YANG Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York:Curran Associates, 2016:2810-2818. [20] TSAI Y H, SHEN X, LIN Z, et al. Deep image harmonization[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:2799-2807. [21] REN W, MA L, ZHANG J, et al. Gated fusion network for single image dehazing[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2018:3253-3261. [22] XIONG W, LUO W, MA L, et al. Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2018:2364-2373. [23] LIU Y, ZHAO G, GONG B, et al. Improved techniques for learning to dehaze and beyond:a collective study[EB/OL].[2018-07-26]. https://arxiv.org/pdf/1807.00202. [24] KINGMA D P, BA J L. ADAM:a method for stochastic optimization[EB/OL].[2018-05-10]. https://simplecore.intel.com/nervana/wp-content/uploads/sites/53/2017/06/1412.6980.pdf. [25] 王一宁, 秦品乐, 李传朋, 等.基于残差神经网络的图像超分辨率改进算法[J]. 计算机应用, 2018, 38(1):246-254. (WANG Y N, QIN P L, LI C P, et al. Improved algorithm of image super resolution based on residual neural network[J]. Journal of Computer Applications, 2018, 38(1):246-254.) [26] 梁中豪, 彭德巍, 金彦旭, 等.基于交通场景区域增强的单幅图像去雾方法[J]. 计算机应用, 2018, 38(5):1420-1426. (LIANG Z H, PENG D W, JIN Y X, et al. Single image dehazing algorithm based on traffic scene region enhancement[J]. Journal of Computer Applications, 2018, 38(5):1420-1426.) |