Papers with Code 아카이브
Image Restoration
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- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Unsupervised Part Discovery via Descriptor-Based Masked Image Restoration with Optimized Constraints
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
Double-Diffusion: Diffusion Conditioned Diffusion Probabilistic Model For Air Quality Prediction
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Wild refitting for black box prediction
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration
TDiR: Transformer based Diffusion for Image Restoration Tasks
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
Reversing Flow for Image Restoration
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior
Exploring Diffusion with Test-Time Training on Efficient Image Restoration
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
Text-Aware Image Restoration with Diffusion Models
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations
M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI
SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training
UniRes: Universal Image Restoration for Complex Degradations
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Image Restoration Learning via Noisy Supervision in the Fourier Domain
Boosting All-in-One Image Restoration via Self-Improved Privilege Learning
IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
Reference-Guided Identity Preserving Face Restoration
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration
BaryIR: Learning Multi-Source Unified Representation in Continuous Barycenter Space for Generalizable All-in-One Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.