[1] [1] LAND E H.The retinex theory of color vision[J].Scientific American,1977,237(6):108-128.

[2] [2] HINES G,RAHMAN Z,JOBSON D,et al.Single-scale retinex using digital signal processors［EB/OL］.(2005-02-22)［2022-02-28］.https://ntrs.nasa.gov/api/ci-tations/20050091487/downloads/20050091487.pdf.

[3] [3] JOSHI K R,KAMATHE R S.Quantification of retinex in enhancement of weather degraded images［C］//International Conference on Audio,Language and Image Processing,July 07-09,2008,Shanghai,China.New York:IEEE,2008:1229-1233.

[4] [4] FU X Y,ZHUANG P X,HUANG Y,et al.A retinex-based enhancing approach for single underwater image［C］//IEEE International Conference on Image Processing,October 27-30,2014,Paris,France.New York:IEEE,2014:4572-4576.

[5] [5] ALEX R S M,SUPRIYA M H.Underwater image enhancement using single scale retinex on a reconfigurable hardware［C］//International Symposium on Ocean Electronics,November 18-20,2015, Kochi,India.New York:IEEE,2015:1-5.

[8] [8] UPLAVIKAR P M,WU Z Y,WANG Z Y.All-in-one underwater image enhancement using domain-adversaral learning［EB/OL］.(2019-05-30)［2022-02-28］.https://arxiv.org/abs/1905.13342v1.

[13] [13] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected crfs[J].Computational Intelligence and Neuroscience,2014,4:357-361.

[14] [14] 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 Pattern Analysis Machine Intelligence,2016,40:834-848.

[15] [15] CHEN L C,ZHU Y,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation［C］//European Conference on Computer Vision(ECCV),September 8-14,2018,Munich,Germany.New York:IEEE,2018:833-851.

[16] [16] ZHANG Z,HUANG J,JIANG T,et al.Semantic segmentation of very high resolution remote sensing image based on multiple band combinations and patchwise scene analysis[J].Journal of Applied Remote Sensing,2020,14(1)：016502.

[23] [23] HUANG Z,ZHAO Y W,LIU Y H,et al.GCAUNet:a group cross-channel attention residual UNet for slice based brain tumor segmentation[J].Biomedical Signal Processing and Control,2021,70:102958.