CVPR2024.md

Awesome-CVPR2024-Low-Level-Vision

整理汇总下今年CVPR底层视觉(Low-Level Vision)相关的论文和代码，括超分辨率，图像去雨，图像去雾，去模糊，去噪，图像恢复，图像增强，图像去摩尔纹，图像修复，图像质量评价，插帧，图像/视频压缩等任务，具体如下。

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CVPR2024官网：https://cvpr.thecvf.com/Conferences/2024

CVPR完整论文列表：https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers

开会时间：2024年6月17日-6月21日

论文接收公布时间：2024年2月27日

【Contents】

• 1.超分辨率(Super-Resolution)
• 2.图像去雨(Image Deraining)
• 3.图像去雾(Image Dehazing)
• 4.去模糊(Deblurring)
• 5.去噪(Denoising)
• 6.图像恢复(Image Restoration)
• 7.图像增强(Image Enhancement)
• 8.图像修复(Inpainting)
• 9.高动态范围成像(HDR Imaging)
• 10.图像质量评价(Image Quality Assessment)
• 11.插帧(Frame Interpolation)
• 12.视频/图像压缩(Video/Image Compression)
• 13.压缩图像质量增强(Compressed Image Quality Enhancement)
• 14.图像去反光(Image Reflection Removal)
• 15.图像去阴影(Image Shadow Removal)
• 16.图像上色(Image Colorization)
• 17.图像和谐化(Image Harmonization)
• 18.视频稳相(Video Stabilization)
• 19.图像融合(Image Fusion)
• 20.其他任务(Others)

1.超分辨率(Super-Resolution)

AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution

• Paper: https://arxiv.org/abs/2404.03296
• Code: https://github.com/Cheeun/AdaBM

A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution

• Paper: https://arxiv.org/abs/2404.15620
• Code: https://github.com/XYLGroup/DKP

APISR: Anime Production Inspired Real-World Anime Super-Resolution

• Paper: https://arxiv.org/abs/2403.01598
• Code: https://github.com/Kiteretsu77/APISR

Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss

• Paper: https://arxiv.org/abs/2404.01692
• Code: https://github.com/JaehaKim97/SR4IR

Bilateral Event Mining and Complementary for Event Stream Super-Resolution

• Paper:
• Code: https://github.com/Lqm26/BMCNet-ESR

Boosting Flow-based Generative Super-Resolution Models via Learned Prior

• Paper: https://arxiv.org/abs/2403.10988
• Code: https://github.com/liyuantsao/FlowSR-LP

Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model

• Paper: https://arxiv.org/abs/2403.17460
• Code: https://github.com/dongrunmin/RefDiff

CAMixerSR: Only Details Need More “Attention”

• Paper: https://arxiv.org/abs/2402.19289
• Code: https://github.com/icandle/CAMixerSR

CFAT: Unleashing Triangular Windows for Image Super-resolution

• Paper: https://arxiv.org/abs/2403.16143
• Code: https://github.com/rayabhisek123/CFAT

Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World

• Paper:

CoSeR: Bridging Image and Language for Cognitive Super-Resolution

• Paper: https://arxiv.org/abs/2311.16512
• Code: https://github.com/VINHYU/CoSeR

CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution

• Paper:
• Code: https://github.com/I2-Multimedia-Lab/CDFormer

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

• Paper: https://arxiv.org/abs/2404.04878
• Code:

Diffusion-based Blind Text Image Super-Resolution

• Paper: https://arxiv.org/abs/2312.08886
• Code:

DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF

• Paper: https://arxiv.org/abs/2404.00874
• Code:

Image Processing GNN: Breaking Rigidity in Super-Resolution

• Paper:

Latent Modulated Function for Computational Optimal Continuous Image Representation

• Paper: https://arxiv.org/abs/2404.16451
• Code: https://github.com/HeZongyao/LMF

Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution

• Paper:

Learning Large-Factor EM Image Super-Resolution with Generative Priors

• Paper:
• Code: https://github.com/jtshou/GPEMSR

Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning

• Paper: https://arxiv.org/abs/2403.02601
• Code: https://github.com/haoyuc/LWay

Navigating Beyond Dropout: An Intriguing Solution towards Generalizable Image Super-Resolution

• Paper: https://arxiv.org/abs/2402.18929v2
• Code: https://github.com/Dreamzz5/Simple-Align

Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

• Paper: https://arxiv.org/abs/2308.06699
• Code: https://github.com/Riga2/NSRD

Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution

• Paper: https://arxiv.org/abs/2404.04785
• Code: https://github.com/GuangYuanKK/DiffMSR

SeD: Semantic-Aware Discriminator for Image Super-Resolution

• Paper: https://arxiv.org/abs/2402.19387
• Code: https://github.com/lbc12345/SeD

SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

• Paper: https://arxiv.org/abs/2311.16518
• Code: https://github.com/cswry/SeeSR

Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution

• Paper: https://arxiv.org/abs/2403.16643
• Code: https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion

SinSR: Diffusion-Based Image Super-Resolution in a Single Step

• Paper: https://github.com/wyf0912/SinSR/blob/main/main.pdf
• Code: https://github.com/wyf0912/SinSR

Super-Resolution Reconstruction from Bayer-Pattern Spike Streams

• Paper:

Text-guided Explorable Image Super-resolution

• Paper: https://arxiv.org/abs/2403.01124

Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts

• Paper: https://arxiv.org/abs/2402.19215
• Code: https://github.com/mandalinadagi/wgsr

Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary

• Paper: https://arxiv.org/abs/2401.08209
• Code: https://github.com/LabShuHangGU/Adaptive-Token-Dictionary

Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer

• Paper: https://arxiv.org/abs/2303.17783

Universal Robustness via Median Random Smoothing for Real-World Super-Resolution

• Paper:

Video Super-Resolution

Enhancing Video Super-Resolution via Implicit Resampling-based Alignment

• Paper: https://github.com/kai422/IART/blob/main/arxiv.pdf
• Code: https://github.com/kai422/IART

FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring

• Paper: https://arxiv.org/abs/2401.03707
• Code: https://github.com/KAIST-VICLab/FMA-Net

Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution

• Paper: https://arxiv.org/abs/2403.17000

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

• Paper: https://arxiv.org/abs/2312.06640
• Code: https://github.com/sczhou/Upscale-A-Video

Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention

• Paper: https://arxiv.org/abs/2401.06312
• Code: https://github.com/LabShuHangGU/MIA-VSR

2.图像去雨(Image Deraining)

Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining

• Paper: https://arxiv.org/abs/2404.01547
• Code: https://github.com/cschenxiang/NeRD-Rain

3.图像去雾(Image Dehazing)

A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint

• Paper: https://arxiv.org/abs/2403.18548
• Code: https://github.com/Xiaofeng-life/SFSNiD

Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing

• Paper: https://arxiv.org/abs/2403.01105
• Code:

ODCR: Orthogonal Decoupling Contrastive Regularization for Unpaired Image Dehazing

• Paper: https://arxiv.org/abs/2404.17825v1
• Code:

Video Dehazing

Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance

• Paper:

4.去模糊(Deblurring)

A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning

• Paper: https://arxiv.org/abs/2403.02611
• Code: https://github.com/PieceZhang/MPT-CataBlur

AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring

• Paper: https://github.com/INVOKERer/AdaRevD/blob/master/AdaRevD.pdf
• Code: https://github.com/INVOKERer/AdaRevD

Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains

• Paper: https://arxiv.org/abs/2403.16205
• Code: https://github.com/VinAIResearch/Blur2Blur

Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring

• Paper:

ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation

• Paper：https://arxiv.org/abs/2312.10998
• Code: https://github.com/plusgood-steven/ID-Blau

LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network

• Paper: https://arxiv.org/abs/2307.09815
• Code: https://github.com/noxsine/LDP

Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring

• Paper: https://arxiv.org/abs/2404.13153
• Code: https://github.com/ChengxuLiu/MISCFilter

Motion Blur Decomposition with Cross-shutter Guidance

• Paper: https://arxiv.org/abs/2404.01120
• Code: https://github.com/jixiang2016/dualBR

Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization

• Paper: https://arxiv.org/abs/2404.12168

Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment

• Paper:
• Code: https://github.com/Leozhangjiyuan/UaSDN

Unsupervised Blind Image Deblurring Based on Self-Enhancement

• Paper:

Video Deblurring

Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring

• Paper:
• Code: https://github.com/huicongzhang/BSSTNet

EVS-assisted joint Deblurring, Rolling-Shutter Correction and Video Frame Interpolation through Sensor Inverse Modeling

• Paper:

Frequency-aware Event-based Video Deblurring for Real-World Motion Blur

• Paper:

Latency Correction for Event-guided Deblurring and Frame Interpolation

• Paper:

5.去噪(Denoising)

LAN: Learning to Adapt Noise for Image Denoising

• Paper:

Robust Image Denoising through Adversarial Frequency Mixup

• Paper:
• Code: https://github.com/dhryougit/AFM

Real-World Mobile Image Denoising Dataset with Efficient Baselines

• Paper:

SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder

• Paper: https://arxiv.org/abs/2403.17502
• Code: https://github.com/zhengdharia/SeNM-VAE

Transfer CLIP for Generalizable Image Denoising

• Paper: https://arxiv.org/abs/2403.15132

Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising

• Paper:

ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light Images

• Paper:

6.图像恢复(Image Restoration)

Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image Restoration

• Paper:

Boosting Image Restoration via Priors from Pre-trained Models

• Paper: https://arxiv.org/abs/2403.06793

CoDe: An Explicit Content Decoupling Framework for Image Restoration

• Paper:

Deep Equilibrium Diffusion Restoration with Parallel Sampling

• Paper: https://arxiv.org/abs/2311.11600
• Code: https://github.com/caojiezhang/DeqIR

Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks

• Paper: https://arxiv.org/abs/2403.00644
• Code: https://github.com/yuhaoliu7456/Diff-Plugin

Distilling Semantic Priors from SAM to Efficient Image Restoration Models

• Paper: https://arxiv.org/abs/2403.16368
• Code:

DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

• Paper: https://arxiv.org/abs/2405.04408
• Code: https://github.com/ZZZHANG-jx/DocRes

HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models

• Paper: https://arxiv.org/abs/2402.15865
• Code: https://github.com/LiPang/HIRDiff

Image Restoration by Denoising Diffusion Models With Iteratively Preconditioned Guidance

• Paper: https://arxiv.org/abs/2312.16519
• Code: https://github.com/tirer-lab/DDPG

Improving Image Restoration through Removing Degradations in Textual Representations

• Paper: https://arxiv.org/abs/2312.17334
• Code: https://github.com/mrluin/TextualDegRemoval

Learning Degradation-unaware Representation with Prior-based Latent Transformations for Blind Face Restoration

• Paper:

Learning Diffusion Texture Priors for Image Restoration

• Paper:

Look-Up Table Compression for Efficient Image Restoration

• Paper:

Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration

• Paper: https://arxiv.org/abs/2312.02918

PFStorer: Personalized Face Restoration and Super-Resolution

• Paper: https://arxiv.org/abs/2403.08436

Restoration by Generation with Constrained Priors

• Paper: https://arxiv.org/abs/2312.17161

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

• Paper: https://arxiv.org/abs/2401.13627
• Code: https://github.com/Fanghua-Yu/SUPIR

Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model

• Paper: https://arxiv.org/abs/2403.11157
• Code: https://github.com/iSEE-Laboratory/DiffUIR

Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence

• Paper: https://arxiv.org/abs/2404.13605
• Code: https://github.com/Riponcs/Turb-Seg-Res

WaveFace: Authentic Face Restoration with Efficient Frequency Recovery

• Paper: https://arxiv.org/abs/2403.12760
• Code:

Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration

• Paper: https://arxiv.org/abs/2311.16845
• Code: https://github.com/zhihefang/wf-diff

7.图像增强(Image Enhancement)

Color Shift Estimation-and-Correction for Image Enhancement

• Paper: https://drive.google.com/file/d/1jZB2rW_I2WLTE5yNA4IZq9wb5p4NNOCR/view
• Code: https://github.com/yiyulics/CSEC

Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance

• Paper:

Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring

• Paper:

FlowIE：Efficient Image Enhancement via Rectified Flow

• Paper:

Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving

• Paper: https://arxiv.org/abs/2404.04804
• Code: https://github.com/jinlong17/LightDiff

Robust Depth Enhancement via Polarization Prompt Fusion Tuning

• Paper: https://arxiv.org/abs/2404.04318
• Code: https://github.com/lastbasket/Polarization-Prompt-Fusion-Tuning

Specularity Factorization for Low Light Enhancement

• Paper: https://arxiv.org/abs/2404.01998

Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach

• Paper: https://arxiv.org/abs/2404.00834
• Code: https://github.com/EthanLiang99/EvLight

ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light Images

• Paper:

Zero-Reference Low-Light Enhancement via Physical Quadruple Priors

• Paper: https://arxiv.org/abs/2403.12933
• Code: https://github.com/daooshee/QuadPrior

Video Enhancement

Binarized Low-light Raw Video Enhancement

• Paper: https://arxiv.org/abs/2403.19944
• Code: https://github.com/zhanggengchen/BRVE

UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement

• Paper: https://arxiv.org/abs/2404.14542
• Code: https://github.com/yzbouc/UVEB

8.图像修复(Inpainting)

Amodal Completion via Progressive Mixed Context Diffusion

• Paper: https://arxiv.org/abs/2312.15540
• Code: https://github.com/k8xu/amodal

Brush2Prompt: Contextual Prompt Generator for Object Inpainting

• Paper:

Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting

• Paper: https://arxiv.org/abs/2403.18186
• Code:

Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting

• Paper: https://arxiv.org/abs/2403.19898
• Code: https://github.com/htyjers/StrDiffusion

Video Inpainting

AVID: Any-Length Video Inpainting with Diffusion Model

• Paper: https://arxiv.org/abs/2312.03816
• Code: https://github.com/zhang-zx/AVID

Towards Language-Driven Video Inpainting via Multimodal Large Language Models

• Paper: https://arxiv.org/abs/2401.10226
• Code: https://github.com/jianzongwu/Language-Driven-Video-Inpainting

9.高动态范围成像(HDR Imaging)

CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment

• Paper: https://arxiv.org/abs/2404.01123
• Code: https://github.com/hmin970922/CLIPtone/

Deep Video Inverse Tone Mapping Based on Temporal Clues

• Paper:
• Code: https://github.com/ye3why/VITM-TC

Generating Content for HDR Deghosting from Frequency View

• Paper: https://arxiv.org/abs/2404.00849

HDRFlow: Real-Time HDR Video Reconstruction with Large Motions

• Paper: https://arxiv.org/abs/2403.03447
• Code: https://github.com/OpenImagingLab/HDRFlow

Perceptual Assessment and Optimization of HDR Image Rendering

• Paper: https://arxiv.org/abs/2310.12877v4
• Code: https://github.com/cpb68/HDRQA/

Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings

• Paper:

Towards Real-World HDR Video Reconstruction: A Large-Scale Benchmark Dataset and A Two-Stage Alignment Network

• Paper: https://arxiv.org/abs/2405.00244
• Code: https://github.com/yungsyu99/Real-HDRV

Zero-Shot Structure-Preserving Diffusion Model for High Dynamic Range Tone Mapping

• Paper:

10.图像质量评价(Image Quality Assessment)

Blind Image Quality Assessment Based on Geometric Order Learning

• Paper:

Boosting Image Quality Assessment through Efficient Transformer Adaptation with Local Feature Enhancement

• Paper: https://arxiv.org/abs/2308.12001
• Code:

Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment

• Paper: https://arxiv.org/abs/2405.04167
• Code:

CLIB-FIQA: Face Image Quality Assessment with Confidence Calibration

• Paper:

Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment

• Paper: https://arxiv.org/abs/2403.10066

Deep Generative Model based Rate-Distortion for Image Downscaling Assessment

• Paper: https://arxiv.org/abs/2403.15139
• Code: https://github.com/Byronliang8/IDA-RD

Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization

• Paper: https://arxiv.org/abs/2403.11397
• Code: https://github.com/YangiD/DefenseIQA-NT

DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

• Paper:

EvalCrafter: Benchmarking and Evaluating Large Video Generation Models

• Paper: https://arxiv.org/abs/2310.11440
• Code: https://github.com/evalcrafter/EvalCrafter

FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment

• Paper: https://arxiv.org/abs/2405.06887
• Code: https://github.com/PKU-ICST-MIPL/FineParser_CVPR2024

KVQ: Kwai Video Quality Assessment for Short-form Videos

• Paper: https://arxiv.org/abs/2402.07220
• Code: https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024

Learned Scanpaths Aid Blind Panoramic Video Quality Assessment

• Paper: https://arxiv.org/abs/2404.00252
• Code: https://github.com/kalofan/AutoScanpathQA

Modular Blind Video Quality Assessment

• Paper: https://arxiv.org/abs/2402.19276
• Code: https://github.com/winwinwenwen77/ModularBVQA

On the Content Bias in Fréchet Video Distance

• Paper: https://arxiv.org/abs/2404.12391
• Code: https://github.com/songweige/content-debiased-fvd

PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild

• Paper:

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

• Paper: https://arxiv.org/abs/2311.06783
• Code: https://github.com/Q-Future/Q-Instruct

11.插帧(Frame Interpolation)

Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images

• Paper: https://arxiv.org/abs/2404.01464
• Code: https://github.com/jungeun122333/UVI-Net

IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation

• Paper:

Perceptual-Oriented Video Frame Interpolation Via Asymmetric Synergistic Blending

• Paper: https://arxiv.org/abs/2404.06692
• Code:

Sparse Global Matching for Video Frame Interpolation with Large Motion

• Paper: https://arxiv.org/abs/2404.06913
• Code: https://github.com/MCG-NJU/SGM-VFI

SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation

• Paper: https://arxiv.org/abs/2308.16876
• Code: https://github.com/neu-vi/SportsSloMo

TTA-EVF: Test-Time Adaptation for Event-based Video Frame Interpolation via Reliable Pixel and Sample Estimation

• Paper:

Video Frame Interpolation via Direct Synthesis with the Event-based Reference

• Paper:

Video Interpolation with Diffusion Models

• Paper: https://arxiv.org/abs/2404.01203

12.视频/图像压缩(Video/Image Compression)

C3: High-performance and low-complexity neural compression from a single image or video

• Paper: https://arxiv.org/abs/2312.02753
• Code: https://github.com/google-deepmind/c3_neural_compression

Generative Latent Coding for Ultra-Low Bitrate Image Compression

• Paper:

Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis

• Paper: https://arxiv.org/abs/2403.16258

Learned Lossless Image Compression based on Bit Plane Slicing

• Paper:

Towards Backward-Compatible Continual Learning of Image Compression

• Paper: https://arxiv.org/abs/2402.18862
• Code: https://gitlab.com/viper-purdue/continual-compression

Video Compression

Task-Aware Encoder Control for Deep Video Compression

• Paper: https://arxiv.org/abs/2404.04848
• Code:

Low-Latency Neural Stereo Streaming

• Paper: https://arxiv.org/abs/2403.17879
• Code:

Neural Video Compression with Feature Modulation

• Paper: https://arxiv.org/abs/2402.17414
• Code: https://github.com/microsoft/DCVC

13.压缩图像质量增强(Compressed Image Quality Enhancement)

CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement

• Paper: https://arxiv.org/abs/2403.10362

Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

• Paper: https://arxiv.org/abs/2402.17200

14.图像去反光(Image Reflection Removal)

Language-guided Image Reflection Separation

• Paper: https://arxiv.org/abs/2402.11874
• Code:

Revisiting Singlelmage Reflection Removal in the Wild

• Paper: https://arxiv.org/abs/2311.17320
• Code: https://github.com/zhuyr97/Reflection_RemoVal_CVPR2024

15.图像去阴影(Image Shadow Removal)

HomoFormer: Homogenized Transformer for Image Shadow Removal

• Paper:
• Code: https://github.com/jiexiaou/HomoFormer

16.图像上色(Image Colorization)

Automatic Controllable Colorization by Imagination

• Paper: https://arxiv.org/abs/2404.05661
• Code: https://github.com/xy-cong/imagine-colorization

Generative Quanta Color Imaging

• Paper: https://arxiv.org/abs/2403.19066

Learning Inclusion Matching for Animation Paint Bucket Colorization

• Paper: https://arxiv.org/abs/2403.18342
• Code: https://github.com/ykdai/BasicPBC

17.图像和谐化(Image Harmonization)

Relightful Harmonization: Lighting-aware Portrait Background Replacement

• Paper: https://arxiv.org/abs/2312.06886
• Code:

Video Harmonization with Triplet Spatio-Temporal Variation Patterns

18.视频稳相(Video Stabilization)

3D Multi-frame Fusion for Video Stabilization

• Paper: https://arxiv.org/abs/2404.12887
• Code:

Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

• Paper: https://arxiv.org/abs/2403.03662
• Code: https://github.com/MKashifAli/MetaVideoStab

19.图像融合(Image Fusion)

Equivariant Multi-Modality Image Fusion

• Paper： https://arxiv.org/abs/2305.11443
• Code: https://github.com/Zhaozixiang1228/MMIF-EMMA

MRFS: Mutually Reinforcing Image Fusion and Segmentation

Neural Spline Fields for Burst Image Fusion and Layer Separation

• Paper: https://arxiv.org/abs/2312.14235
• Code: https://github.com/princeton-computational-imaging/NSF

Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion

Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image Fusion

Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion

• Paper: https://arxiv.org/abs/2403.16387
• Code: https://github.com/XunpengYi/Text-IF

Task-Customized Mixture of Adapters for General Image Fusion

• Paper: https://arxiv.org/abs/2403.12494
• Code: https://github.com/YangSun22/TC-MoA

20.其他任务(Others)

Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening

• Paper: https://arxiv.org/abs/2404.07543
• Code: https://github.com/Duanyll/CANConv

DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model

• Paper: https://arxiv.org/abs/2311.11417
• Code: https://github.com/PAN083/DiffSCI

DPU: Dual Prior Unfolding for Snapshot Compressive Imaging

• Paper:
• Code: https://github.com/ZhangJC-2k/DPU

Dual-Camera Smooth Zoom on Mobile Phones

• Paper: https://arxiv.org/abs/2404.04908
• Code: https://github.com/ZcsrenlongZ/ZoomGS

Dual-scale Transformer for Large-scale Single-Pixel Imaging

• Paper: https://arxiv.org/abs/2404.05001
• Code: https://github.com/Gang-Qu/HATNet-SPI

Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal

• Paper: https://arxiv.org/abs/2403.07684
• Code: https://github.com/scott-yjyang/DiffTTA

Language-driven All-in-one Adverse Weather Removal

• Paper: https://arxiv.org/abs/2312.01381
• Code:

Learning to Remove Wrinkled Transparent Film with Polarized Prior

• Paper: https://arxiv.org/abs/2403.04368
• Code: https://github.com/jqtangust/FilmRemovalww

Misalignment-Robust Frequency Distribution Loss for Image Transformation

• Paper: https://arxiv.org/abs/2402.18192
• Code: https://github.com/eezkni/FDL

On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation

• Paper: https://arxiv.org/abs/2404.08540
• Code: https://github.com/agneet42/lang_depth

ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

• Paper: https://arxiv.org/abs/2312.13313
• Code: https://github.com/woo525/ParamISP

RecDiffusion: Rectangling for Image Stitching with Diffusion Models

• Paper: https://arxiv.org/abs/2402.18192
• Code: https://github.com/lhaippp/RecDiffusion

Residual Denoising Diffusion Models

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/nachifur/RDDM

Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling

• Paper: https://arxiv.org/abs/2404.11884
• Code: https://github.com/HUST-IAL/CoTF

SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image

• Paper: https://arxiv.org/abs/2403.20018
• Code: https://github.com/WU-CVGL/SCINeRF

Seeing Motion at Nighttime with an Event Camera

• Paper: https://arxiv.org/abs/2404.11884
• Code: https://github.com/Liu-haoyue/NER-Net

Shadow Generation for Composite Image Using Diffusion Model

• Paper: https://arxiv.org/abs/2403.15234
• Code: https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2

SSR: Improving Spectral Snapshot Reconstruction with Spatial-Spectral Rectification

• Paper:
• Code: https://github.com/ZhangJC-2k/SSR

持续更新~

参考

Related Collections(相关整理)

• Awesome-CVPR2021-Low-Level-Vision
• Awesome-CVPR2020-Low-Level-Vision
• Awesome-ECCV2020-Low-Level-Vision
• Awesome-Low-Level-Vision-Research-Groups
• Awesome-AIGC-Research-Groups
• Awesome-CVPR2024-AIGC