激光与光电子学进展, 2020, 57 (14): 141024, 网络出版: 2020-07-28   

基于级联残差生成对抗网络的低照度图像增强 下载: 970次

Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network
作者单位
西安建筑科技大学理学院, 陕西 西安 710055
引用该论文

陈清江, 屈梅. 基于级联残差生成对抗网络的低照度图像增强[J]. 激光与光电子学进展, 2020, 57(14): 141024.

Qingjiang Chen, Mei Qu. Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141024.

参考文献

[1] Pizer S M, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations[J]. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355-368.

[2] Reza A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2004, 38(1): 35-44.

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

[4] Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround Retinex[J]. IEEE Transactions on Image Processing, 1997, 6(3): 451-462.

[5] Jobson D J, Rahman Z, Woodell G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997, 6(7): 965-976.

[6] Fu X Y, Zeng D L, Huang Y, et al. A fusion-based enhancing method for weakly illuminated images[J]. Signal Processing, 2016, 129: 82-96.

[7] 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.

[8] Ying ZQ, LiG, Ren YR, et al. A new low-light image enhancement algorithm using camera response model[C]∥2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017. Venice. IEEE, 2017.

[9] Ren XT, Li MD, Cheng WH, et al. Joint enhancement and denoising method via sequential decomposition[C]∥2018 IEEE International Symposium on Circuits and Systems (ISCAS), May 27-30, 2018. Florence. IEEE, 2018: 1- 5.

[10] 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.

[11] Li C Y, Guo J C, Porikli F, et al. LightenNet: a convolutional neural network for weakly illuminated image enhancement[J]. Pattern Recognition Letters, 2018, 104: 15-22.

[12] 马红强, 马时平, 许悦雷, 等. 基于深度卷积神经网络的低照度图像增强[J]. 光学学报, 2019, 39(2): 0210004.

    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): 0210004.

[13] IsolaP, Zhu JY, Zhou TH, et al. Image-to-image translation with conditional adversarial networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017. Honolulu, HI. IEEE, 2017: 1125- 1134.

[14] 唐贤伦, 杜一铭, 刘雨微, 等. 基于条件深度卷积生成对抗网络的图像识别方法[J]. 自动化学报, 2018, 44(5): 855-864.

    Tang X L, Du Y M, Liu Y W, et al. Image recognition with conditional deep convolutional generative adversarial networks[J]. Acta Automatica Sinica, 2018, 44(5): 855-864.

[15] Chen XY, Wang SA. Superpixel segmentation based on delaunay triangulation[C]∥2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), November 28-30, 2016. Nanjing, China. IEEE, 2016: 1- 6.

[16] Mannos J, Sakrison D. The effects of a visual fidelity criterion of the encoding of images[J]. IEEE Transactions on Information Theory, 1974, 20(4): 525-536.

[17] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[18] Ma K D, Zeng K, Wang Z. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3345-3356.

[19] GoodfellowI, Pouget-AbadieJ, MirzaM, et al. Generative adversarial nets[C]∥Advances in Neural Information Processing Systems, 2014: 2672- 2680.

陈清江, 屈梅. 基于级联残差生成对抗网络的低照度图像增强[J]. 激光与光电子学进展, 2020, 57(14): 141024. Qingjiang Chen, Mei Qu. Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141024.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!