MBLLEN: Low-Light Image/Video Enhancement Using CNNs

By Feifan Lv, Feng Lu, Jianhua Wu, Chongsoon Lim. British Machine Vision Conference, 2018

794 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/70cb4bdd05cccc1f99cf690582e66b7637b81da7

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Citations in the corpus

Seeing Motion in the Dark (2019, background): “Methods have been developed that can enhance brightness and contrast of images and videos acquired in moderately dim environments [12, 34, 30, 36, 17, 31].”
Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023, methodology): “…LIE meth-ods, which can be divided into the following three categories: traditional methods (SDD [9], STAR [34]), supervised approaches (MBLLEN [20], RetinexNet [32], GALD-Net [30], KinD [38], DRBN [36], URetinexNet [33]), and unsupervised methods (Zero-DCE [7], RRDNet [41], RUAS [18], SCI…”
Generative Diffusion Prior for Unified Image Restoration and Enhancement (2023, background): “6 [52] Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim.”
Low-Light Image Enhancement with Normalizing Flow (2021, methodology): “Multi-scale features are adopted (Shen et al. 2017; Tao et al. 2017; Lv et al. 2018; Ren et al. 2019) to obtain better visual quality.”
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement (2023, methodology): “With the rapid progress of deep learning, CNN [15, 16, 21, 22, 25, 34, 36, 38, 45, 48, 60, 65, 67] has been widely used in low-light image enhancement.”
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, methodology): “[6], RetinexNet [7], LightenNet [39], MBLLEN [23]), and one GANbased method (EnlightenGAN [9]).”
Benchmarking Low-Light Image Enhancement and Beyond (2021, methodology): “Then, the enhanced results are obtained by further processing 123 2018), the multi-scale features are injected into the multi-branch architecture toformbetterlow-lightenhancementresults.”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “52 MBLLEN [51] 24.”
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation (2019, methodology): “ework which learns jointly from stylized images and unlabeled real images of increasing darkness and exploits the prior knowledge from a map. There is a rich literature on low-light image enhancement [27], [28], [29], which is also relevant to our work. However, its focus is more on the low-level go”