[26] Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh. Deep local parametric filters for image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12826-12835, 2020. 6, 7
[27] Ravi Teja Mullapudi, Andrew Adams, Dillon Sharlet, Jonathan Ragan-Kelley, and Kayvon Fatahalian. Automatically scheduling halide image processing pipelines. ACM Transactions on Graphics (TOG), 35(4):1-11, 2016. 2
[28] A van Oppenheim, Ronald Schafer, and Thomas Stockham. Nonlinear filtering of multiplied and convolved signals. IEEE transactions on audio and electroacoustics, 16(3):437-466, 1968. 1
[29] Sylvain Paris, Samuel W Hasinoff, and Jan Kautz. Local laplacian filters: Edge-aware image processing with a laplacian pyramid. ACM Trans. Graph., 30(4):68, 2011. 2, 5
[30] Jongchan Park, Joon-Young Lee, Donggeun Yoo, and In So Kweon. Distort-and-recover: Color enhancement using deep reinforcement learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5928-5936, 2018. 2
[31] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. Openreview.net, 2017. 7
[32] Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris, Marc Levoy, Saman Amarasinghe, and Frédo Durand. Decoupling algorithms from schedules for easy optimization of image processing pipelines. ACM Transactions on Graphics (TOG), 31(4):1-12, 2012. 2
[33] Erik Reinhard and Kate Devlin. Dynamic range reduction inspired by photoreceptor physiology. IEEE transactions on visualization and computer graphics, 11(1):13-24, 2005. 2
[34] Erik Reinhard, Michael Stark, Peter Shirley, and James Ferwerda. Photographic tone reproduction for digital images. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 267-276, 2002. 1, 2
[35] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6849-6857, 2019. 2, 6, 7
[36] Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, and Youliang Yan. Real-time image enhancer via learnable spatial-aware 3d lookup tables. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2471-2480, 2021. 2, 4, 6, 7, 8
[37] Zhicheng Yan, Hao Zhang, Baoyuan Wang, Sylvain Paris, and Yizhou Yu. Automatic photo adjustment using deep neural networks. ACM Transactions on Graphics (TOG), 35(2):1-15, 2016. 2
[38] Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2058-2073, 2020. 2, 4, 6, 7, 9
[39] Fengyi Zhang, Hui Zeng, Tianjun Zhang, and Lin Zhang. Clut-net: Learning adaptively compressed representations of 3dluts for lightweight image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6493-6501, 2022. 2, 6, 7
[40] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 6
[41] Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, and Ales Leonardis. Image demoireing with learnable bandpass filters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3636-3645, 2020. 2