A Debye CZT-based wave-optics pipeline generates lens-diverse synthetic defocus blur datasets that improve cross-device deblurring generalization over existing real and synthetic data.
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Introduces the TUB dataset of 1320 real turbid underwater images and PCD metric showing strong correlation with instance segmentation performance where standard metrics fail.
A pixel-space network that injects frozen DINOv3 semantic features through spatially continuous per-pixel modulation reports state-of-the-art PSNR/SSIM/LPIPS on LOLv2-Real (29.08/0.902/0.089) and the best average across four paired LLIE benchmarks.
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
ENLIGHT is a zero-shot optimization framework for low-light image enhancement using global illumination adjustment followed by shadow-aware local refinement to achieve competitive quality with lower inference time.
citing papers explorer
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Realistic Compound-Lens Defocus Blur Synthesis
A Debye CZT-based wave-optics pipeline generates lens-diverse synthetic defocus blur datasets that improve cross-device deblurring generalization over existing real and synthetic data.
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Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
Introduces the TUB dataset of 1320 real turbid underwater images and PCD metric showing strong correlation with instance segmentation performance where standard metrics fail.
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PixIE: Prompted Pixel-Space Low-Light Image Enhancement
A pixel-space network that injects frozen DINOv3 semantic features through spatially continuous per-pixel modulation reports state-of-the-art PSNR/SSIM/LPIPS on LOLv2-Real (29.08/0.902/0.089) and the best average across four paired LLIE benchmarks.
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Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
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Enlight: Fast Low-Light Image Enhancement via Multi-Objective Optimization and Shadow-Aware Refinement
ENLIGHT is a zero-shot optimization framework for low-light image enhancement using global illumination adjustment followed by shadow-aware local refinement to achieve competitive quality with lower inference time.
- Spectral Progressive Diffusion for Efficient Image and Video Generation