计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6): 297-305.doi: 10.11896/jsjkx.240300004

• 计算机图形学&多媒体 • 上一篇    下一篇

边缘和颜色信息引导下的高分辨率低光图像增强算法

张玲1,2, 李振宇1   

  1. 1 武汉科技大学计算机科学与技术学院 武汉 430065
    2 智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学) 武汉 430065
  • 收稿日期:2024-02-29 修回日期:2024-06-25 出版日期:2025-06-15 发布日期:2025-06-11
  • 通讯作者: 张玲(zhling@wust.edu.cn)
  • 基金资助:
    国家自然科学基金(61902286);湖北省自然科学基金(2023AFB615)

Edge and Color Information Guided High-resolution Low-light Image Enhancement Algorithm

ZHANG Ling1,2, LI Zhenyu1   

  1. 1 School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China
    2 Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems,Wuhan University of Science and Technology,Wuhan 430065,China
  • Received:2024-02-29 Revised:2024-06-25 Online:2025-06-15 Published:2025-06-11
  • About author:ZHANG Ling,born in 1986,Ph.D,associate professor.Her main research interests include image and video editing,and computational photography.
  • Supported by:
    National Natural Science Foundation of China(61902286) and Hubei Provincial Natural Science Foundation(2023AFB615).

PDF (PC)

80

摘要： 设备捕捉高分辨率图像的能力对图像处理提出了新的挑战,现有的低光图像增强算法多是针对低分辨率图像设计的,在处理高分辨率图像时,存在细节不清晰、颜色失真等问题。利用图像自身包含的纹理信息和颜色信息,提出了一种边缘和颜色信息引导的高分辨率低光照图像增强算法。为改善卷积神经网络局部特征学习的局限性,引入了边缘解码器,有助于捕获图像中远距离的关键信息,提高对边界语义信息的编码。此外,为处理高分辨率图像,在上下文注意力块中引入了稀疏注意力机制,集中关注图像中的重要信息,以有效减少噪声干扰。另一方面,颜色解码器有效利用了低光图像自身的色度线索,提升了颜色信息恢复的准确性。

关键词: 高分辨率, 图像增强, 低光照

Abstract: The ability of the device to capture high-resolution images poses a new challenge to image processing,and most of the existing low-light image enhancement algorithms are designed for low-resolution images,and there are problems such as unclear details and color distortion when dealing with high-resolution images.Using the texture information and color information contained in the image itself,an edge and color information guided high-resolution low-light image enhancement algorithm is proposed.To improve the limitation of local feature learning of convolutional neural network,an edge decoder is introduced,which helps to capture the key information in the image at a long distance and improves the encoding of semantic information at the boundary.In addition,in order to deal with high-resolution images,a sparse attention mechanism is introduced in the context attention blocks,which focuses on the important information in the image and effectively reduces noise interference.On the other hand,the color decoder effectively utilizes the chromaticity cues of the low-light image itself to improve the accuracy of color information recovery.

Key words: High resolution, Image enhancement, Low light

中图分类号: 

  • TP301
[1]WANG B,LIANG Y D,LIU Z,et al.Study on Unsupervised Image Dehazing and Low-light Image Enhancement Algorithms Based on Luminance Adjustment[J].Computer Science,2023,50(1):123-130.
[2]LI L,LIU X L,ZHAO Y,et al.Low Light Image Fusion Detection Method Based on Lego Filter and SSD[J].Computer Science,2021,48(7):213-218.
[3]PISANO E D,ZONG S,HEMMINGER B M,et al.Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculations in dense mammograms[J].Journal of Digital Imaging,1998,11(4):193-200.
[4]HOU L X,NIE F Y,WAN L Y.Multiscale adaptive Gamma correction for low-light image enhancement[J].Journal of Yunnan University(Natural Sciences Edition),2023,45(1):57-66.
[5]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.
[6]WANG W,WEI C,YANG W,et al.Gladnet:Low-light en-hancement network with global awareness[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition(FG 2018).IEEE,2018:751-755.
[7]WANG R,XU X,FU C W,et al.Seeing dynamic scene in the dark:A high-quality video dataset with mechatronic alignment[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:9700-9709.
[8]LIU R,MA L,ZHANG J,et al.Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:10561-10570.
[9]ZHAO Z,XIONG B,WANG L,et al.RetinexDIP:A unifieddeep framework for low-light image enhancement[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,32(3):1076-1088.
[10]LI J,FENG X,HUA Z.Low-light image enhancement via progressive-recursive network[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,31(11):4227-4240.
[11]WANG R,XU X,FU C W,et al.Seeing dynamic scene in the dark:A high-quality video dataset with mechatronic alignment[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:9700-9709.
[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.
[13]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.
[14]GUO C,LI C,GUO J,et al.Zero-reference deep curve estimation for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1780-1789.
[15]ZHU A,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.
[16]COTOGNI M,CUSANO C.TreEnhance:A tree search method for low-light image enhancement[J].Pattern Recognition,2023,136:109249.
[17]ZHANG Y,LIU H,DING D.A cross-scale framework for low-light image enhancement using spatial-spectral information[J].Computers and Electrical Engineering,2023,106:108608.
[18]WANG T,ZHANG K,SHEN T,et al.Ultra-high-definition low-light image enhancement:A benchmark and transformer-based method[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023,37(3):2654-2662.
[19]TRIANTAFYLLIDOU D,MORAN S,MCDONAGH S,et al.Low light video enhancement using synthetic data produced with an intermediate domain mapping[C]//Computer Vision－ECCV 2020:16th European Conference,Glasgow,UK,Part XIII 16.Springer International Publishing,2020:103-119.
[20]LU K,ZHANG L.TBEFN:A two-branch exposure-fusion network for low-light image enhancement[J].IEEE Transactions on Multimedia,2020,23:4093-4105.
[21]WU W,WENG J,ZHANG P,et al.Uretinex-net:Retinex-based deep unfolding network for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:5901-5910.
[22]WU J F,ZHAO X G,ZHOU Q,et al.Contrastive Learning for Low-light Image Enhancement[J].Computer Science,2023,50(S1):525-530.
[23]ZHAO M H,WEN Y C,DU S L,et al.Low-light image en-hancement algorithm based on illumination and scene texture attention map[J].Journal of Image and Graphics,2024,29(4):862-874.
[24]HE L,YI Z H,XIE Y F,et al.Fast enhancement method for low light images guided by Retinex prior [J].Acta Automatica Sinica,2024,50(5):1-12.
[25]JIANG Z T,QIN L L,Qin J Q,et al.Low-light image enhancement method based on MDARNet[J].Journal of Software,2021,32(12):3977-3991.
[26]SENTHILKUMARAN N,THIMMIARAJA J.Histogramequalization for image enhancement using MRI brain images[C]//2014 World Congress on Computing and Communication Technologies.IEEE,2014:80-83.
[27]YUN S H,KIM J H,KIM S.Image enhancement using a fusion framework of histogram equalization and Laplacian pyramid[J].IEEE Transactions on Consumer Electronics,2010,56(4):2763-2771.
[28]SENTHILKUMARAN N,THIMMIARAJA J.Histogramequalization for image enhancement using MRI brain images[C]//2014 World Congress on Computing and Communication Technologies.IEEE,2014:80-83.
[29]SURESHA M,RAGHUKUMAR D S,KUPPA S.Kuma-raswamy distribution based bi-histogram equalization for enhancement of microscopic images[J].International Journal of Image and Graphics,2022,22(1):2250003.
[30]SINGH P,MUKUNDAN R,DE RYKE R.Feature enhancement in medical ultrasound videos using contrast-limited adaptive histogram equalization[J].Journal of DiIital imaging,2020,33:273-285.
[31]LAND E H.The retinex theory of color vision[J].Scientific american,1977,237(6):108-129.
[32]DONG X,PANG Y,WEN J.Fast efficient algorithm for enhancement of low lighting video[C]//2011 IEEE International Conference on Multimedia and Expo.2011.
[33]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.
[34]JAIN V,SEUNG S.Natural image denoising with convolutional networks[C]//Proceedings of the 22nd International Conference on Neural Information Processing Systems.2008:769-776.
[35]LV F,LU F,WU J,et al.MBLLEN:Low-Light Image/Video Enhancement Using CNNs[C]//BMVC.2018.
[36]REN W,LIU S,MA L,et al.Low-light image enhancement via a deep hybrid network[J].IEEE Transactions on Image Proces-sing,2019,28(9):4364-4375.
[37]ZHU M,PAN P,CHEN W,et al.Eemefn:Low-light image enhancement via edge-enhanced multi-exposure fusion network[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020,34(7):13106-13113.
[38]LI J,LI J,FANG F,et al.Luminance-aware pyramid network for low-light image enhancement[J].IEEE Transactions on Multimedia,2020,23:3153-3165.
[39]WANG L W,LIU Z S,SIU W C,et al.Lightening network for low-light image enhancement[J].IEEE Transactions on Image Processing,2020,29:7984-7996.
[40]LIM S,KIM W.DSLR:Deep stacked Laplacian restorer for low-light image enhancement[J].IEEE Transactions on Multimedia,2020,23:4272-4284.
[41]WEI C,WANG W,YANG W,et al.Deep retinex decomposition for low-light enhancement[J].arXiv:1808.04560,2018.
[42]WANG Y,WAN R,YANG W,et al.Low-light image enhancement with normalizing flow[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022,36(3):2604-2612.
[43]MA L,MA T,LIU R,et al.Toward fast,flexible,and robustlow-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:5637-5646.
[44]WANG Y,YU Y,YANG W,et al.Exposurediffusion:Learning to expose for low-light image enhancement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2023:12438-12448.
[45]YANG S,DING M,WU Y,et al.Implicit neural representation for cooperative low-light image enhancement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2023:12918-12927.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发