The Academic Perspective Procedia publishes Academic Platform symposiums papers as three volumes in a year. DOI number is given to all of our papers.
Publisher : Academic Perspective
Journal DOI : 10.33793/acperpro
Journal eISSN : 2667-5862
[1] Shakibania H, Raoufi S, Khotanlou H. CDAN: convolutional dense attention-guided network for low-light image enhancement. Digit Signal Processing 2025;156:104802.
[2] Wang H, Chen Y, Cai Y, Chen L, Li Y, Sotelo MA, et al. SFNet-N: an improved SFNet algorithm for semantic segmentation of low-light autonomous driving road scenes. Transactions on Intelligent Transportation Systems 2022;23(11):21405–17.
[3] Hashmi KA, Kallempudi G, Stricker D, Afzal MZ. FeatEnhancer: enhancing hierarchical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision 2023;6725–35.
[4] Fan Y, Wang Y, Liang D, Chen Y, Xie H, Wang FL, et al. Low-FaceNet: face recognition- driven low-light image enhancement IEEE Transactions on Instrumentation and Measurement. 2024.
[5] Kim W. Low-light image enhancement: a comparative review and prospects. IEEE Access 2022;10:84535–57.
[6] Li M, Liu J, Yang W, Sun X, Guo Z. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transaction on Image Processing 2018;27(6):2828–41.
[7] Ren X, Yang W, Cheng WH, Liu J. LR3M: robust low-light enhancement via low-rank regularized Retinex model. IEEE Transaction on Image Processing 2020;29:5862–76.
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[10] Fu X, Zeng D, Huang Y, Zhang XP, Ding X. A weighted variational model for simultaneous reflectance and illumination estimation. . In Proceedings of The IEEE Conference on Computer Vision And Pattern Recognition 2016;2782–90.
[11] Guo X, Li Y, Ling H. LIME: low-light image enhancement via illumination map estimation. IEEE Transaction on Image Processing 2016;26(2):982–93.
[12] Ren X, Li M, Cheng WH, Liu J. Joint enhancement and denoising method via sequential decomposition. . In 2018 IEEE International Symposium on Circuits and Systems (ISCAS) 2018;1–5.
[13] Jobson DJ, Rahman ZU, Woodell GA. Properties and performance of a center/surround Retinex. IEEE Transaction on Image Processing 1997;6(3):451–62.
[14] Wang S, Zheng J, Hu HM, Li B. Naturalness preserved enhancement algorithm for non- uniform illumination images. . IEEE Transaction on Image Processing 2013;22(9):3538–48.
[15] Tao L, Zhu C, Xiang G, Li Y, Jia H, Xie X. LLCNN: a convolutional neural network for low- light image enhancement. Proc IEEE Visual Communication and Image Processing (VCIP) 2017:1–4.
[16] Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, et al. EnlightenGAN: deep light enhancement without paired supervision. IEEE Transaction on Image Processing 2021;30:2340–9.
[17] Guo C, Li C, Guo J, Loy CC, Hou J, Kwong S, Cong R. Zero-reference deep curve estimation for low-light image enhancement. . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020;1780–9.
[18] Cai Y, Bian H, Lin J, Wang H, Timofte R, Zhang Y. Retinexformer: one-stage Retinex-based transformer for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023;12504–13.
[19] Guo J, Ma J, García-Fernández ÁF, Zhang Y, Liang H. A survey on image enhancement for low-light images. Heliyon 2023;9(4):e14558.
[20] KS GR, Biswas A, Patel MS, Prasad BP. Deep multi-stage learning for HDR with large object motions. In 2019 IEEE International Conference on Image Processing (ICIP) 2019;4714–8.
[21] Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J. A fusion-based enhancing method for weakly illuminated images. Signal Processing 2016;129:82–96.
[22] Ancuti C, Ancuti CO, Haber T, Bekaert P. Enhancing underwater images and videos by fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2012;81–8.
[23] Wang W, Yan D, Wu X, He W, Chen Z, Yuan X, Li L. Low-light image enhancement based on virtual exposure. Signal Processing: Image Communication 2023;118:117016.
[24] He K, Sun J, Tang X. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012;35(6):1397–409.
[25] Cai J, Gu S, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transaction on Image Processing 2018;27(4):2049–62.
[26] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Transaction on Image Processing 2004;13(4):600–12.
[27] Fu X, Zeng D, Huang Y, Zhang XP, Ding X. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016;2782–90.
[28] Guo X, Li Y, Ling H. LIME: low-light image enhancement via illumination map estimation. IEEE Transaction on Image Processing 2016;26(2):982–93.
[29] Li M, Liu J, Yang W, Sun X, Guo Z. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transaction on Image Processing 2018;27(6):2828–41.
[30] Wang R, Zhang Q, Fu CW, Shen X, Zheng WS, Jia J. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019;6849–57.
[31] Ma L, Ma T, Liu R, Fan X, Luo Z. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022;5637–46.
[32] Jeon JJ, Park JY, Eom IK. Low-light image enhancement using gamma correction prior in mixed color spaces. Pattern Recognition 2024;146:110001.