计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 147-154.doi: 10.11896/jsjkx.221200074
高仁, 郝世杰, 郭艳蓉
GAO Ren, HAO Shijie, GUO Yanrong
摘要: 暗光环境下成像往往受到低照度和成像噪声等多种因素干扰,所得图片的视觉质量往往较低。当前各类暗光增强方法多侧重于改善可视度,却常忽略了保持增强结果真实感这一同样重要的目标。为解决该问题,提出了一种自适应去噪保真的无监督暗光图像增强方法,旨在高效便捷地实现改善图像可视度和去噪保真两个目标。模型由暗光增强阶段和去噪保真阶段组成。在暗光增强阶段,构建无监督图像分解模块和光照增强模块,实现改善可视度的目标;在去噪保真阶段,基于前一阶段所得的光照分布来自动构造成对训练数据,驱动去噪模块抑制原本昏暗处的噪声并保持原本明亮处的细节,实现增强结果保真的目标。实验结果表明,相比其他暗光增强方法,所提方法在改善可视度和去噪保真之间能够取得良好的均衡。该模型无须事先采集或准备“昏暗-明亮”成对图像来进行训练,且具有较小的模型尺寸和较快的计算速度,实用性良好。
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[1]CAI B L,XU X M,GUO K L,et al.A Joint Intrinsic-Extrinsic Prior Model for Retinex[C]//International Conference on Computer Vision(ICCV).IEEE,2017:4020-4029. [2]FU X Y,ZENG D L,HUANG Y,et al.A fusion-based enhancing method for weakly illuminated images[J].Signa Proces-sing,2016,129:82-96. [3]LI C Y,GUO C L,HAN LH,et al.Low-Light Image and Video Enhancement Using Deep Learning:A Survey[J].arXiv:2104.10729,2021. [4]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. [5]GUO X J,LI Y,LING H B.LIME:Low-Light Image Enhancement via Illumination Map Estimation[J].IEEE Transactions on Image Processing,2017,26(2):982-993. [6]LI M D,LIU J Y,YANG W H,et al.Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model[J].IEEE Transactions on Image Processing,2018,27(6):2828-2841. [7]ZHANG Y H,ZHANG J W,GUO X J.Kindling the Darkness:A Practical Low-light Image Enhancer[C]//ACM International Conference on Multimedia,Association for Computing Machi-nery.2019:1632-1640. [8]WEI C,WANG W J,YANG W H,et al.Deep Retinex Decomposition for Low-Light Enhancement[C]//British Machine Vision Conference.IEEE,2018:4020-4029. [9]WANG R X,ZHANG Q,FU C W,et al.Underexposed PhotoEnhancement Using Deep Illumination Estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2019:6842-6850. [10]JIANG Y F,GONG X Y,LIU D,et al.EnlightenGAN:Deep Light Enhancement Without Paired Supervision[J].IEEE Transactions on Image Processing,2021,30:2340-2349. [11]LEE C,LEE C,KIM C S.Contrast Enhancement Based on La-yered Difference Representation of 2D Histograms[J].IEEE Transactions on Image Processing,2013,22(12):5372-5384. [12]REN X T,YANG W H,CHENG WH,et al.LR3M:Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model[J].IEEE Transactions on Image Processing,2020,29:5862-5876. [13]HAO S J,GUO Y R,WEI Z.Lightness-aware contrast enhancement for images with different illumination conditions[J].Multimedia Tools and Application,2019,78:3817-3830. [14]HAO S J,HAN X,GUO Y R,et al.Low-Light Image Enhancement With Semi-Decoupled Decomposition[J].IEEE Transactions on Multimedia,2020,22(12):3025-3038. [15]REN W Q,LIU S F,XU Q Q,et al.Low-Light Image Enhancement via a Deep Hybrid Network[J].IEEE Transactions on Image Processing,2019,28(9):4364-4375. [16]MA K D,DUANMU Z F,ZHU H W,et al.Deep Guided Lear-ning for Fast Multi-Exposure Image Fusion[J].IEEE Transactions on Image Processing,2019,29:2808-2819. [17]MA K D,LI H,YONG H W,et al.Robust Multi-ExposureImage Fusion:A Structural Patch Decomposition Approach[J].IEEE Transactions on Image Processing,2017,26(5):2519-2532. [18]YANG W H,WANG S Q,FANG Y M,et al.From Fidelity to Perceptual Quality:A Semi-Supervised Approach for Low-Light Image Enhancement[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2020:3060-3069. [19]CHEN C,CHEN Q F,XU J,et al.Learning to See in the Dark[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.2018:3291-3300. [20]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer Assisted Intervention.2015:234-241. [21]HE K M,SUN J,TANG X O.Single image haze removal using dark channel prior[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:1956-1963. [22]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations.2014:1956-1963. [23]GUO C L,LI C Y,GUO J C,et al.Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2020:1777-1786. [24]CAI J R,GU S H,ZHANG L.Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images[J].IEEE Transactions on Image Processing,2018,27(4):2049-2062. [25]LV F F,LU F,WU J H,et al.MBLLEN:Low-Light Image/Video Enhancement Using CNNs[C]//British Machine Vision Conference.2018. [26]WANG S H,ZHENG J,HU H M,et al.Naturalness preserved enhancement algorithm for non-uniformillumination images[J].IEEE Transactions on Image Processing,2013,22(9):3538-3548. [27]FU X Y,LIAO Y H,ZENG D L,et al.A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation[J].IEEE Transactions on Image Proces-sing,2015,24(12):4965-4977. [28]ZHU A Q,ZHANG L,SHEN Y,et al.Zero-Shot Restoration of Underexposed Images via Robust Retinex Decomposition[C]//2020 IEEE International Conference on Multimedia and Expo(ICME).IEEE,2020:1-6. [29]DABOV K,FOI A,KATKOVNIK V,et al.Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering[J].IEEE Transactions on Image Processing,2007,16(8):2080-2095. [30]ZHANG K,ZUO W M,CHEN Y J,et al.Beyond a Gaussian Denoiser:Residual Learning of Deep CNN for Image Denoising[J].IEEE Transactions on Image Processing,2017,26(7):3142-3155. [31]YU S,PARK B,JEONG J.Deep Iterative Down-Up CNN for Image Denoising[C]//2019 IEEE/CVF Conference on Compu-ter Vision and Pattern Recognition Workshops(CVPRW).IEEE.2019:2095-2103. [32]SHEN R H,ZHOU M R,LING S.Image Enhancement Technology Based on Improved FA Algorithm and Incomplete Beta Function[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2023,40(2):57-63. [33]WANG M M,PENG D L Retinex-ADNet:a Low-light Image Enhancement System[J].Journal of Chinese Computer Systems.2022,43(2):367-371. |
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