Papers Image Restoration
“Image Restoration” 태그가 달린 논문 1,805편 · 필터 해제
Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal
Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robustness of outdoor vision systems. Existing unified restoration models …
Contrastive LearningImage RestorationConvergent Plug-and-Play Image Restoration with Annealed Noise Levels
Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical implementations often decrease the denoiser noise level $σ$ along iter…
Image RestorationGeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations
Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifie…
Image RestorationCloud RemovalImIR: Image-Instruction Tuning for All-in-One Image Restoration
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing model to restoration us…
Image RestorationGraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration
The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing methods rely on predefined multi-scale hierarchies and aggregate featur…
Image RestorationUniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically prioritize modeling inter-task heterogeneity (…
Multi-Task LearningImage RestorationLearning to Restore More: Continual Capability Expansion for Pretrained Image Restoration Models
Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually train additional models or jointly retrain the original model with bot…
Image RestorationBridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspection. This paper investigates whether degradation-aware image restorat…
Image RestorationGAN-Diff : Coupling Pretrained WGAN-GP Features with Conditional Diffusion U-Nets
Generative adversarial networks (GANs) can provide efficient image generation, while diffusion models offer high-quality image restoration but require iterative sampling. This paper presents a hybrid GAN-guided diffusion…
Image RestorationImage GenerationDPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration
All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling and lack low-level visual priors during re…
Image RestorationMagnifiQ: Patch-aware Text Guided Progressive Upscaling for High-Resolution Image Restoration
High-resolution image restoration from degraded inputs is challenging because it must preserve global structural consistency while recovering fine-grained local details, especially at 4K resolution where direct diffusion…
Image RestorationFlow-Map Distillation on Relation Manifolds for Image Restoration
Knowledge distillation for image restoration typically aligns intermediate features or relation matrices between teacher and student networks as static targets, ignoring the dynamic structure of the knowledge transfer pr…
Knowledge DistillationImage RestorationMDTD-ArtIR: Benchmarking Image Editing and Restoration Models for Art Image Restoration under Texture-Overlay Degradations
Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, struggle with preserving color and texture, or fail to leverage partia…
Prompt EngineeringImage RestorationImage EditingCollaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification
We propose a novel collaborative approach for face super-resolution (SR) and robust person re-identification from sequential or multi-view facial images. Traditional SR methods often suffer from blurring and distortion i…
Person Re-IdentificationImage RestorationCoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribu…
Image RestorationWhat to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration
All-in-one image restoration aims to handle diverse degradations within a unified framework. Existing methods commonly encode heterogeneous degradation conditions in a shared latent space, where degradation-related cues …
Image RestorationNoise-Free One-Step LoRA for Task-Driven Image Restoration with Diffusion Priors
Degraded images not only reduce visual quality but also impair downstream high-level vision tasks. Task-driven image restoration (TDIR) addresses this issue by jointly optimizing restoration quality and task performance.…
Image RestorationScaleResfusion: Residual Rectified Flow based on Residual Vector Field
Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations. Although recent diffusion-based methods have substantially improved perceptual quality, their current…
parameter-efficient fine-tuningImage RestorationCausal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents
Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Existing agents typically formulate restoration as a tool-using process, …
Image RestorationThe RealDefocus Benchmark for Defocus Deblurring
Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation, but rigorous and reproducible evaluation remains challenging due to the scarcity of realistic, high-resolu…
Image Restoration