Papers Image Dehazing
“Image Dehazing” 태그가 달린 논문 328편 · 필터 해제
Backbone-Agnostic Stochastic Perturbation Learning for End-to-End Real-World Image Dehazing
Real-world paired image dehazing remains challenging because haze degradation is spatially non-uniform, illumination-dependent, and physically ambiguous even when haze-free references are available. Existing end-to-end r…
Image DehazingRTE-FM-Dehazer: Radiative Transfer Equation Inspired Flow Matching for Real-World Image Dehazing
Single-image dehazing aims to recover a clear scene from a hazy image and is generally formulated as an image-to-image translation task; however, it faces two limitations. Its performance depends heavily on the haze-form…
Image-to-Image TranslationDomain GeneralizationImage DehazingBridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance
Visibility distance is critical to maritime navigational safety because it determines the effective observation range of shipborne and shore-based monitoring systems. Under hazy conditions, degraded visual information sh…
Image Quality AssessmentImage RestorationObject DetectionImage DehazingTowards UAV Image Dehazing: A UAV Atmospheric Scattering Model, Benchmark, and Geometry-Aware Deep Unfolding Network
In UAV applications, haze significantly obscures distant details and weaken structural information, hindering the recovery of details. Current UAV scenarios still face two key challenges: (i) paired hazy/clean images fro…
Image Dehazing6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving
Remote sensing images are frequently degraded by adverse weather conditions, particularly clouds and haze, which severely impair downstream applications. Existing restoration methods typically rely on computationally hea…
Image RestorationImage DehazingIncepDeHazeGAN: Novel Satellite Image Dehazing
Dehazing is a technique in computer vision for enhancing the visual quality of images captured in cloudy or foggy conditions. Dehazing helps to recover clear, high-quality images from haze-affected remote sensing data. I…
Image DehazingPRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing
Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying color shifts, and the scarcity of paired rea…
Image DehazingCLIP-Guided Data Augmentation for Night-Time Image Dehazing
Nighttime image dehazing faces a more complex degradation pattern than its daytime counterpart, as haze scattering couples with low illumination, non-uniform lighting, and strong light interference. Under limited supervi…
Data AugmentationImage DehazingHistoFusionNet: Histogram-Guided Fusion and Frequency-Adaptive Refinement for Nighttime Image Dehazing
Nighttime image dehazing remains a challenging low-level vision problem due to the joint presence of haze, glow, non-uniform illumination, color distortion, and sensor noise, which often invalidate assumptions commonly u…
Representation LearningImage DehazingTransmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazing
Nighttime images captured under hazy conditions suffer from severe quality degradation, including low visibility, color distortion, and reduced contrast, caused by the combined effects of atmospheric scattering, absorpti…
Image DehazingRemote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects
Remote sensing images (RSIs) are frequently degraded by haze, fog, and thin clouds, which obscure surface reflectance and hinder downstream applications. This study presents the first systematic and unified survey of RSI…
Image DehazingBilevel Layer-Positioning LoRA for Real Image Dehazing
Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mecha…
Image DehazingEquivariant Learning for Unsupervised Image Dehazing
Image Dehazing (ID) aims to produce a clear image from an observation contaminated by haze. Current ID methods typically rely on carefully crafted priors or extensive haze-free ground truth, both of which are expensive o…
Image DehazingUDPNet: Unleashing Depth-based Priors for Robust Image Dehazing
Image dehazing has witnessed significant advancements with the development of deep learning models. However, most existing methods focus solely on single-modal RGB features, neglecting the inherent correlation between sc…
Computational EfficiencyDepth EstimationImage DehazingAPI: Empowering Generalizable Real-World Image Dehazing via Adaptive Patch Importance Learning
Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when applied to complex real-world hazy scenes, p…
Data AugmentationImage DehazingFourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing
Image dehazing is crucial for reliable visual perception, yet it remains highly challenging under real-world non-uniform haze conditions. Although Transformer-based methods excel at capturing global context, their quadra…
Image DehazingLearning Implicit Neural Degradation Representation for Unpaired Image Dehazing
Image dehazing is an important task in the field of computer vision, aiming at restoring clear and detail-rich visual content from haze-affected images. However, when dealing with complex scenes, existing methods often s…
Image RestorationImage Dehazing4KDehazeFlow: Ultra-High-Definition Image Dehazing via Flow Matching
Ultra-High-Definition (UHD) image dehazing faces challenges such as limited scene adaptability in prior-based methods and high computational complexity with color distortion in deep learning approaches. To address these …
Image DehazingGUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing
Image dehazing is a restoration task that aims to recover a clear image from a single hazy input. Traditional approaches rely on statistical priors and the physics-based atmospheric scattering model to reconstruct the ha…
Representation LearningImage DehazingUnleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, the massive computational burden imposed b…
Image Dehazing