Rain-Free and Residue Hand-in-Hand: A Progressive Coupled Network for Real-Time Image Deraining

10.1109/CVPR.2019.00835

10.1109/ICCV.2017.511

wei, 2018, Deep retinex decomposition for low-light enhancement, Proc BMVC, 155

yu, 2018, BDD100K: A diverse driving dataset for heterogeneous multitask learning, arXiv 1805 04687

10.1609/aaai.v34i07.6701

10.1109/TIP.2018.2867951

10.1109/CVPR42600.2020.00288

10.1109/TIP.2006.877407

10.1609/aaai.v34i07.6865

10.1109/CVPR42600.2020.00324

10.1007/978-3-642-33715-4_54

feifan lv, 2018, MBLLEN: Low-light image/video enhancement using CNNs, Proc BMVC, 220

10.1109/ICCV.2019.00200

mehta, 2018, Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation, Proc ECCV, 552

10.1109/CVPR.2018.00716

10.1109/ICCVW.2017.71

10.1609/aaai.v34i07.6706

eboli, 2020, Structured and localized image restoration, arXiv 2006 09261

sifre, 2014, Rigid-motion scattering for image classification, 1, 3

ding, 2021, RepVGG: Making VGG-style ConvNets great again, arXiv 2101 03697

10.1109/CVPR.2019.00396

10.1109/CVPR.2018.00194

10.1109/CVPR.2018.00068

ding, 2020, Image quality assessment: Unifying structure and texture similarity, arXiv 2004 07728

ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc ICML, 448

10.1109/CVPR.2019.00400

krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Proc Adv Neural Inf Process Syst (NIPS), 1097

10.1109/CVPR.2019.00821

redmon, 2018, YOLOv3: An incremental improvement, arXiv 1804 02767

howard, 2017, MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv 1704 04861

10.1109/ICCV.2017.322

10.1109/CVPR.2017.549

10.1109/CVPR.2018.00132

10.1109/CVPR.2019.00406

10.1109/CVPR42600.2020.00837

10.1109/TIP.2021.3064229

10.1007/s11263-020-01421-z

10.1007/s11263-020-01428-6

10.1007/s11263-020-01416-w

yang, 2021, End-toend rain removal network based on progressive residual detail supplement, IEEE Trans Multimedia

10.1007/978-3-642-33266-1_8

10.1109/TPAMI.2020.2995190

mei, 2020, Pyramid attention networks for image restoration, arXiv 2004 13824

zamir, 2020, Learning enriched features for real image restoration and enhancement, Proc ECCV, 492

10.1109/TCSVT.2019.2920407

10.1016/j.image.2014.06.006

10.1109/LSP.2012.2227726

10.1109/TCSVT.2020.3044887

10.1109/TPAMI.2020.3042298

10.1109/CVPR.2017.618

zhang, 2018, Image super-resolution using very deep residual channel attention networks, Proc ECCV, 286

wang, 2019, An effective two-branch model-based deep network for single image deraining, arXiv 1905 05404

10.1109/CVPR.2019.00060

ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, Proc Conf Med Image Comput Comput -Assist Intervent, 234

10.1109/TPAMI.2015.2439281

10.1109/TIP.2011.2179057

10.1109/TMM.2013.2284759

li, 2017, Single image deraining using scale-aware multi-stage recurrent network, arXiv 1712 06830

10.1109/WACV.2017.145

guo, 2020, Zero-reference deep curve estimation for low-light image enhancement, Proc CVPR, 1777

10.1109/TIP.2020.3048625

10.1109/CVPR.2019.00701

10.1109/CVPR.2017.186

he, 2020, Conditional sequential modulation for efficient global image retouching, Proc ECCV, 12358, 679

10.1109/CVPR.2018.00079

10.1145/3343031.3350926

10.1145/3240508.3240636

10.1109/TIP.2016.2639450

10.1109/TIP.2017.2691802

10.1109/ICCV.2005.253

10.1109/CVPR.2019.00860

10.1007/978-3-030-01234-2_16

10.1109/83.557356

10.1016/j.cviu.2019.05.003

10.1109/CVPR.2019.00941

zhang, 2019, Residual non-local attention networks for image restoration, Proc ICLR, 1

10.1109/TNNLS.2019.2926481

10.1109/CVPR42600.2020.00313

10.1109/CVPR42600.2020.00597

jiang, 2021, Degrade is upgrade: Learning degradation for low-light image enhancement, arXiv 2103 10621

10.1109/TIP.2021.3051462

10.1515/9783110524116

10.1109/TIP.2006.881969

10.1109/TCSVT.2017.2748150

10.1109/TCSVT.2018.2880223