Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network

Qingjiang Chen; Mei Qu

doi:10.3788/LOP57.141024

[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, 39, 355-368(1987).

[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, 38, 35-44(2004).

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

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

[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, 6, 965-976(1997).

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

[7] Guo X J, Li Y, Ling H B. LIME: low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing, 26, 982-993(2017).

[8] Ying Z Q, Li G, Ren Y R 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 X T, Li M D, Cheng W H 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, 1-5(2018).

[10] Lore K G, Akintayo A, Sarkar S. LLNet: a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 61, 650-662(2017).

[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, 104, 15-22(2018).

[12] 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, 39, 0210004(2019).

[13] Isola P, Zhu J Y, Zhou T H 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, 1125-1134(2017).

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

[15] Chen X Y, Wang S A. 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, 1-6(2016).

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

[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, 13, 600-612(2004).

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

[19] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. [C]∥Advances in Neural Information Processing Systems, 2672-2680(2014).