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官网链接#xff1a;https://cvpr2023.thecvf.com/ 会议时间#xff1a;2023年6月18日-6月22日#xff0c;加拿大温哥华 CVPR 2023统计数据#xff1a;
提交#xff1a;9155篇论文接… 目录 CVPR 2023图像超分任意尺度超分盲超分 视频超分特殊场景 总结参考资料 CVPR 2023
官网链接https://cvpr2023.thecvf.com/ 会议时间2023年6月18日-6月22日加拿大温哥华 CVPR 2023统计数据
提交9155篇论文接受2359篇论文25.8%的接受率亮点235篇论文占录取论文的10%占提交论文的2.6%获奖候选人12篇论文占录取论文的0.51%占提交论文的0.13%
现将超分辨率方向上接收的论文汇总如下遗漏之处还请大家斧正。
图像超分
N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution Paper: https://arxiv.org/abs/2211.11436Code: https://github.com/rami0205/NGramSwinKeywords: Transformer, Lightweight Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation Paper: https://arxiv.org/abs/2211.13676Code: https://github.com/seungho-snu/SROOE Activating More Pixels in Image Super-Resolution Transformer Paper: https://arxiv.org/abs/2205.04437Code: https://github.com/XPixelGroup/HATKeywords: Transformer Burstormer: Burst Image Restoration and Enhancement Transformer Paper: https://arxiv.org/abs/2304.01194Code: http://github.com/akshaydudhane16/BurstormerKeywords: Burst super-resolution Generative Diffusion Prior for Unified Image Restoration and Enhancement Paper: https://arxiv.org/abs/2304.01247Keywords: Unified image recovery Tunable Convolutions with Parametric Multi-Loss Optimization Paper: https://arxiv.org/abs/2304.00898 Omni Aggregation Networks for Lightweight Image Super-Resolution Paper: https://arxiv.org/abs/2304.10244Code: https://github.com/Francis0625/Omni-SRKeywords: Lightweight CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input Paper: https://arxiv.org/abs/2304.06454Keywords: Large Input Image Super-Resolution Using T-Tetromino Pixels Paper: https://arxiv.org/abs/2111.09013 Spectral Bayesian Uncertainty for Image Super-resolution Paper: Memory-friendly Scalable Super-resolution via Rewinding Lottery Ticket Hypothesis Paper:News: PAMI中心8项研究成果被计算机视觉顶级会议CVPR2023录用Keywords: Lightweight
任意尺度超分
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution Paper: https://arxiv.org/abs/2303.05156Keywords: Arbitrary-Scale, Flow Super-Resolution Neural Operator Paper: https://arxiv.org/abs/2303.02584Code: https://github.com/2y7c3/Super-Resolution-Neural-OperatorKeywords: Arbitrary-Scale OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution Paper: https://arxiv.org/abs/2303.01091Keywords: Arbitrary-scale Human Guided Ground-truth Generation for Realistic Image Super-resolution Paper: https://arxiv.org/abs/2303.13069Code: https://github.com/ChrisDud0257/HGGTKeywords: RealSR Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution Paper: https://arxiv.org/abs/2303.16513Code: https://github.com/jaroslaw1007/CLIT Implicit Diffusion Models for Continuous Super-Resolution Paper: https://arxiv.org/abs/2303.16491Code: https://github.com/ree1s/idm CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution Paper: https://arxiv.org/abs/2212.04362Code: https://github.com/caojiezhang/CiaoSRKeywords: Attention, Implicit Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit Paper:Code: https://github.com/neuralchen/EQSR
盲超分
Better “CMOS” Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution Paper: https://arxiv.org/abs/2304.03542 Learning Generative Structure Prior for Blind Text Image Super-resolution Paper: https://arxiv.org/abs/2303.14726Code: https://github.com/csxmli2016/MARCONet
视频超分
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution Paper: https://arxiv.org/abs/2303.13767Code: http://github.io/cvpr23/egvsrKeywords: Implicit Neural Representations Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting Paper: https://arxiv.org/abs/2303.08331Code: https://github.com/coulsonlee/STDO-CVPR2023.git Consistent Direct Time-of-Flight Video Depth Super-Resolution Paper: https://arxiv.org/abs/2211.08658Keywords: dToF Compression-Aware Video Super-Resolution Paper: Structured Sparsity Learning for Efficient Video Super-Resolution Paper: https://arxiv.org/abs/2206.07687Code: https://github.com/Zj-BinXia/SSL
特殊场景
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild Paper: https://arxiv.org/abs/2302.07864Keywords: Diffusion, Wild Learning to Zoom and Unzoom Paper: https://arxiv.org/abs/2303.15390Code: https://tchittesh.github.io/lzu/Keywords: Image Resampling Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution Code: https://github.com/WenjinGuo/DAEMKeywords: Hyperspectral OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer Paper: https://arxiv.org/abs/2302.03453Code: https://github.com/Fanghua-Yu/OSRTKeywords: Omnidirectional Image Cross-Guided Optimization of Radiance Fields with Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis Paper: Guided Depth Super-Resolution by Deep Anisotropic Diffusion Paper: https://arxiv.org/abs/2211.11592Code: https://github.com/prs-eth/Diffusion-Super-ResolutionKeywords: Depth image, Diffusion CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending Paper:Keywords: Light FieldAuthor: http://staff.ustc.edu.cn/~zwxiong/ B-spline Texture Coefficients Estimator for Screen Content Image Super-Resolution Paper: https://ipl.dgist.ac.kr/BTC_cvpr23.pdfCode: https://github.com/ByeongHyunPak/btcKeywords: Screen Content Image Spatial-Frequency Mutual Learning for Face Super-Resolution Paper:Keywords: Face Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution Paper:Keywords: Mobile Image Zero-Shot Dual-Lens Super-Resolution Paper:Code: https://github.com/XrKang/ZeDuSRKeywords: Zero-Shot, Dual-Lens
总结
从本届接收的论文来看超分方向上目前主要聚焦于任意尺度超分 Arbitrary-Scale SR。
参考资料
CVPR2023最新信息及论文下载Papers/Codes/Project/PaperReadingDemos/直播分享论文分享会等Awesome-Super-ResolutionCVPR 2023 Accepted Papers
