A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

By Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, Xinghao Ding. Computer Vision and Pattern Recognition, 2016

1,030 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/57632f82553f34bce21cc8419bc5381d50096592

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Enriched Features for Real Image Restoration and Enhancement (2020, methodology): “SRIE [34] KinD [124] MIRNet (Ours) Ground-truth”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, background): “SRIE [23] develops a weighted vibrational model for illumination map estimation.”
Benchmarking Low-Light Image Enhancement and Beyond (2021, methodology): “For real images, BPDHE, BIMEF, JED, SRIE, Robust, and DeepUPE’s results are under-exposed.”
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network (2021, background): “SRIE [12] proposed a weighted variational model to estimate reﬂectance and illumination at the same time.”
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “…including seven traditional non-learning methods (BIMEF (Ying, Li, and Gao 2017), FEA (Dong et al. 2011), LIME (Guo, Li, and Ling 2016), MF (Fu et al. 2016a), NPE (Wang et al. 2013), SRIE (Fu et al. 2016b), MSRCR (Jobson, Rahman, and Woodell 1997)), three supervised CNN-based methods…”
Beyond Brightening Low-light Images (2021, methodology): “The state-of-the-art methods of BIMEF (Ying et al. 2017), SRIE (Fu et al. 2016), CRM (Ying et al. 2018), Dong (Dong et al. 2011), LIME (Guo et al. 2017), MF (Fu et al. 2016), RRM (Li et al. 2018), Retinex-Net (Wei et al. 2018), DUPE (Wang et al. 2019), DPE (Chen et al. 2018), GLAD (Wang et al. 2018)”
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement (2023, methodology): “Similar to [15, 18, 49], we regard R as a well-exposed image.”
Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, methodology): “Fu et al. [2] proposed a weighted variational model to decompose the illumination layer and the reﬂectance layer simultaneously.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “In addition, Fu et al. proposed a weighted variation model for the simultaneous reﬂectivity and illumination estimation (SRIE) of observed images; this model can precisely retain the estimated reﬂectivity while inhibiting noise to some extent [126].”
Low-Light Image Enhancement with Normalizing Flow (2021, neutral): “(Ying, Li, and Gao 2017) 15.95 0.6386 0.4573 DeepUPE (Wang et al. 2019a) 13.19 0.4902 0.4634 JED (Ren et al. 2018) 16.73 0.6817 0.3899 LIME (Guo, Li, and Ling 2016) 14.07 0.5274 0.4021 SICE (Cai, Gu, and Zhang 2018) 18.06 0.7094 0.5078 LLNet (Lore et al. 2017) 17.57 0.7388 0.4021 SRIE (Fu et al. 201”
MSR-net: Low-light Image Enhancement Using Deep Convolutional Network (2017, methodology): “[10] pro-pose a new weighted variational model to estimate both the reﬂection and the illumination.”
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, methodology): “We compare Zero-DCE with several state-of-the-art methods: three conventional methods (SRIE [7], LIME [8], Li et al.”
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, methodology): “Figure 1(c) shows the result by a state-of-the-art SICE method [3], which only takes an under-exposure image as input (the image with red box shown at Figure 1(a)).”
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, methodology): “Different ways [103], [104] are used to decompose the signal and diverse priors [105]–[108] are applied to realize better light adjustment and noise suppression.”
Underexposed Photo Enhancement Using Deep Illumination Estimation (2019, methodology): “Unlike existing work [11, 14], we model S as a multi-channel (R, G, B) data instead of single-channel one to increase its competence in modeling color enhancement, especially for handling the nonlinearity across different color channels.”
Learning to Restore Low-Light Images via Decomposition-and-Enhancement (2020, methodology): “(c) WVM [14] (d) DeepUPE [40] (e) CAPE [25] (f) LIME [20]”
Kindling the Darkness: A Practical Low-light Image Enhancer (2019, methodology): “8101 Metrics SRIE [12] Retinex-Net [21] MSR [8] NPE [9] GLAD [36] KinD PSNR 11.”
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, methodology): “14a), Zero-DCE yields natural exposure and clear details while SRIE [18], LIME [19], LightenNet [39], MBLLEN [23], Wang et al.”