Single Image Dehazing
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Benchmarks
Most implemented
Contrastive Learning for Compact Single Image Dehazing
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
Deep Variational Bayesian Modeling of Haze Degradation Process
Papers
Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling
Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight and low-latency models. Existing approaches either rely on fragile p…
Single Image DehazingObject DetectionDehaze-GaussianImage: Zero-Shot Dehazing via Efficient 2D Gaussian Splatting Representation
Existing single image dehazing methods are often constrained by computational redundancy in pixel-level optimization and the lack of physical interpretability in implicit neural networks. These limitations hinder the bal…
Single Image DehazingZero-Shot LearningFi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing
Single image dehazing continues to be hindered by the loss of high-frequency details and the difficulty of accurate physical scattering modeling. To address these issues, we propose Fi-Gaussian, a frequency-aware implici…
Single Image DehazingPoint CloudsMulti-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusion
Existing single image dehazing methods have demonstrated satisfactory performance on homogeneous thin-haze images; however, they often struggle with non-homogeneous hazy images that exhibit spatially varying haze concent…
Single Image DehazingImage EnhancementZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing
Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNN networks struggle to learn robust priors for dense and non-homogeneous haze. Conver…
Computational EfficiencySingle Image DehazingU-Net-Like Spiking Neural Networks for Single Image Dehazing
Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques,…
Single Image Dehazing