GLADNet: Low-Light Enhancement Network with Global Awareness

By Wenjing Wang, Chen Wei, Wenhan Yang, Jiaying Liu. IEEE International Conference on Automatic Face & Gesture Recognition, 2018

331 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/81f0d0773dbdb95a6018c1290a7bbb533905c044

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In Computer Vision and Pattern Recognition

Citations in the corpus

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement (2020, methodology): “We compared it qualitatively and quantitatively with twelve recently-proposed state-of-the-art methods including LIME [9], SDD [10], MBLLEN [20], GLADNet [33], RetinexNet [2], EnGAN [12], SSIENet [45], KinD [46], DeepUPE [31], ZeroDCE [8], FIDE [34], and DRBN [37].”
MAXIM: Multi-Axis MLP for Image Processing (2022, background): “Visual examples for image low-light enhancement on the LOL dataset [96] between Retinex [96], GLAD [90], KinD [119], EnlightenGAN [36], MIRNet [107], and MAXIM-2S.”
Learning Enriched Features for Real Image Restoration and Enhancement (2020, methodology): “Method BIMEF CRM Dong LIME MF RRM SRIE Retinex-Net MSR NPE GLAD KinD MIRNet [116] [117] [29] [40] [34] [67] [34] [107] [54] [101] [102] [124] (Ours)”
Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023, methodology): “SDD [9], RetinexNet [32], GALDNet [30], KinD [38], URetinexNet [33], ZeroDCE [7], RRDNet [41], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours).”
Kindling the Darkness: A Practical Low-light Image Enhancer (2019, methodology): “The state-of-the-art methods of BIMEF [33], SRIE [12], CRM [34], Dong [11], LIME [16], MF [14], RRM [21], Retinex-Net [30], GLAD [29], MSR [18] and NPE [28] are involved as the competitors.”
Beyond Brightening Low-light Images (2021, methodology): “After retraining on the FiveK dataset, the performance of four methods have all been improved on this dataset.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “In [261], a global illumination-aware and detail-preserving network (GLADNet) was designed.”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “48 GLADNet [75] 24.”