计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 199-209.doi: 10.11896/jsjkx.210400092
赵征鹏1, 李俊钢1, 普园媛1,2
ZHAO Zheng-peng1, LI Jun-gang1, PU Yuan-yuan1,2
摘要： 利用传统Retinex模型进行低照度图像分解和增强时,需要人工不断地进行参数调试以达到最优解,这会降低整个过程的效率。此外,现有的基于Retinex理论的低照度图像增强方法在进行图像增强时未能很好地兼顾反射分量和光照分量,会存在低照度图反射分量噪点多、光照分量亮度低且细节不够突出的问题。基于此,提出了一种数据驱动的深层网络来学习低照度图像的分解和增强,通过端到端的网络训练来进行模型参数的学习。该网络先将低照度图分解为反射分量和光照分量,针对反射分量噪点多的问题,采用改进的去噪卷积神经网络(New Denoising Convolutional Neural Network,NDnCNN)模型进行去噪;针对光照分量亮度低、细节不够突出的问题,引入卷积块注意力模型(Convolutional Block Attention Model,CBAM)进行细节增强并指导网络进行光照分量的修正;最后用去噪后的反射分量和修正后的光照分量进行图像重建。经测试,增强后的低照度图亮度提升,细节突出,信息丰富,图像失真小且真实自然。
中图分类号:
| [1] REFAEL C.Digital image processing(2 Edition)[M].Translated by RUAN Q Q.Beijing:Publishing House of Electronics Industry,2003. [2] MOU Q,WEI Y Y,LI J,et al.Research on Improved Retinex Low Illumination Image Enhancement Algorithm[J].Journal of Harbin Engineering University,2018,39(12):2001-2010. [3] LI Q Z,LIU Q.Adaptive enhancement algorithm for low illumination image based on wavelet transform[J].Chinese Journal of Lasers,2015,42(2):280-286. [4] ZHANG Y J.Image Engineering Volume-Image Processing and Analysis[M].Beijing:Tsinghua University Press,2006. [5] JAIN A.Fundamentals of Digital Image Processing[M].Englewood Cliffs,NJ:Prentice Hall,1989. [6] IBRAHIM H,KONG N S P.Brightness Preserving DynamicHistogram Equalization for Image Contrast Enhancement[J].IEEE Transactions on Consumer Electronics,2007,53(4):1752-1758. [7] CHAO W,YE Z.Brightness preserving histogram equalization with maximum entropy:a variational perspective[J].IEEE Transactions on Consumer Electronics,2005,51(4):1326-1334. [8] CHEN S D,RAMLI A R.Minimum mean brightness error Bi-histogram equalization in contrast enhancement[J].IEEE Transactions on Consumer Electronics,2003,49(4):1310. [9] YUE H,YANG J,SUN X,et al.Contrast enhancement based on intrinsic image decomposition[J].IEEE Transactions on Image Processing,2017,26(8):3981-3994. [10] LAND E H,MCCANN J J.Lightness and retinextheory[J].Journal of the Optical Society of America,1971,61(1):1-11. [11] JOBSON D J,RAHMAN Z U,WOODELL G A.Properties and Performance of a Center/Surround Retinex[J].IEEE Transactions on Image Processing,1997,6(3):451-462. [12] RAHMAN Z U,JOBSON D J,WOODELL G W.Multiscale retinex for color rendition and dynamic range compression[J].Proc Spie,1996,2847:183-191. [13] COOPER T J,BAQAI F A.Analysis and extensions of theFrankle-McCann Retinex algorithm[J].Journal of Electronic Imaging,2004,13(1):85-92. [14] LORE K G,AKINTAYO A,SARKAR S.LLNet:A DeepAutoencoder Approach to Natural Low-light Image Enhancement[J].Pattern Recognition,2017,61:650-662. [15] MA H Q,MA S P,XU Y L,et al.Low-light image enhancement based on deep convolutional neural network[J].Acta Optica Sinica,2019,39(2):91-100. [16] SHEN L,YUE Z,FENG F,et al.MSR-net:Low-light Image Enhancement Using Deep Convolutional Network[J].arXiv:1711.02488. [17] WEI C,WANG W,YANG W,et al.Deep retinex decomposition for low-light enhancement[C]//British Machine Vision Confe-rence(BMVC).2018. [18] ZHANG Y,ZHANG J,GUO X,et al.Kindling the Darkness:A Practical Low-Light Image Enhancer[C]//ACM Multimedia.2019:1632-1640. [19] WU R Y,WANG D X,YUAN H C,et al.Low illuminationimage enhancement based on multi-branch fully convolutional neural network[J].Progress in Laser and Optoelectronics,2020,57(14):197-207. [20] LI J H,WNAG K.A Low-light Image Enhancement Method Based on Convolutional Neural Network[J].Journal of Jiangxi University of Science and Technology,2020,41(5):73-79. [21] CHAN S H,KHOSHABEH R,GIBSON K B,et al.An Augmented Lagrangian Method for Total Variation Video Restoration[J].IEEE Transactions on Image Processing,2011,20(11):3097-3111. [22] ZHANG K,ZUO W,CHEN Y,et al.Beyond a Gaussian Denoi-ser:Residual Learning of Deep CNN for Image Denoising[J].IEEE Transactions on Image Processing,2016,26(7):3142-3155. [23] WOO S,PARK J,LEE J Y,et al.Cbam:Convolutionalblock attention module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:3-19. [24] MITTAL A,SOUNDARARAJAN R.Making a ‘CompletelyBlind’ Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212. [25] DANG-NGUYEN D T,PASQUINI C,CONOTTER V,et al.RAISE:a raw images dataset for digital image forensics[C]//ACM Multimedia Systems Conference.ACM,2015:219-224. [26] WANG S H,ZHENG J,HUH M,et al.Naturalness preserved enhancement algorithm for non-uniform illumination images[J].IEEE Transactions on Image Processing,2013,22(9):3538-3548. [27] 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. [28] 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. [29] ROTH S,BLACK M J.Fields of experts[J].International Journal of Computer Vision,2009,82(2):205-229. [30] LOH Y P,CHAN C S.Getting to know low-light images with the Exclusively Dark dataset[J].Computer Vision and Image Understanding,2019,178:30-42. [31] MA K,ZENG K,WANG Z.Perceptual Quality Assessment for Multi-Exposure Image Fusion[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2015,24(11):3345-3356. [32] LV F F,LU F,WU J H,et al.MBLLEN:Low-light Image/Video Enhancement Using CNNs[C]//British Machine Vision Conference(BMVC).2018. [33] FU X,ZENG D,HUANG Y,et al.A Weighted VariationalModel for Simultaneous Reflectance and Illumination Estimation[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2016:2782-2790. [34] YING Z,LI G,GAO W.A bio-inspired multi-exposure fusion framework for low-light image enhancement[J].arXiv:1711.00591,2017. [35] LI M,LIU J,YANG W,et al.Structure-Revealing Low-LightImage Enhancement Via Robust Retinex Model[J].IEEE Transactions on Image Processing,2018,27(6):2828-2841. [36] Al-AMEEN Z.Nighttime image enhancement using a new illumination boost algorithm[J].Image Processing,IET,2019,13(8):1314-1320. [37] DAI Q,PU Y F,RAHMAN Z,et al.Fractional-Order FusionModel for Low-Light Image Enhancement[J].Symmetry,2019,11(4):574-591. [38] LU K,ZHANG L.TBEFN:A Two-Branch Exposure-Fusion Network for Low-Light Image Enhancement[J].IEEE Transactions on Multimedia,2021,23:4093-4105. [39] HAO S,HAN X,GUO Y,et al.Low-Light Image Enhancement with Semi-Decoupled Decomposition[J].IEEE Transactions on Multimedia,2020,22(12):3025-3038. |
| [1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [2] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [3] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [4] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [5] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [6] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [7] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
| [8] | 刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179 |
| [9] | 徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085 |
| [10] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [11] | 杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236 |
| [12] | 杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169 |
| [13] | 王杉, 徐楚怡, 师春香, 张瑛. 基于CNN-LSTM的卫星云图云分类方法研究 Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM 计算机科学, 2022, 49(6A): 675-679. https://doi.org/10.11896/jsjkx.210300177 |
| [14] | 孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217 |
| [15] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
|
||
首页