PhySe-RPO enables diffusion-based surgical smoke removal by converting restoration into a stochastic policy optimized with physics consistency and CLIP semantic rewards under limited supervision.
Single image haze removal using dark channel prior.IEEE transactions on pat- tern analysis and machine intelligence, 33(12):2341–2353
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An RL-trained lightweight agent uses MLLM perceptual rewards to perform efficient label-free image restoration, matching SOTA on full-reference metrics and surpassing prior work on no-reference metrics.
UniSER is a unified diffusion transformer foundation model that removes diverse soft image degradations by training on a large curated dataset of semi-transparent occlusions with fine-grained controls.
CLIP-guided selection of external data plus staged NAFNet training and inference fusion provides an effective pipeline for nighttime image dehazing in the NTIRE 2026 challenge.
SmokeGS-R uses refined dark channel prior for pseudo-clean supervision to train 3DGS geometry, followed by ensemble-based appearance harmonization, achieving PSNR 15.21 and outperforming baselines on smoke restoration challenge data.
A multi-stage pipeline of restoration, dehazing, MLLM enhancement and 3D Gaussian Splatting with MCMC averaging achieves first place in the NTIRE 2026 smoke-degraded novel view synthesis track.
The NTIRE 2026 challenge reports measurable progress in 3D reconstruction pipelines that handle real-world low-light and smoke degradation via the RealX3D benchmark.
citing papers explorer
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PhySe-RPO: Physics and Semantics Guided Relative Policy Optimization for Diffusion-Based Surgical Smoke Removal
PhySe-RPO enables diffusion-based surgical smoke removal by converting restoration into a stochastic policy optimized with physics consistency and CLIP semantic rewards under limited supervision.
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Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback
An RL-trained lightweight agent uses MLLM perceptual rewards to perform efficient label-free image restoration, matching SOTA on full-reference metrics and surpassing prior work on no-reference metrics.
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UniSER: A Foundation Model for Unified Soft Effects Removal
UniSER is a unified diffusion transformer foundation model that removes diverse soft image degradations by training on a large curated dataset of semi-transparent occlusions with fine-grained controls.
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CLIP-Guided Data Augmentation for Night-Time Image Dehazing
CLIP-guided selection of external data plus staged NAFNet training and inference fusion provides an effective pipeline for nighttime image dehazing in the NTIRE 2026 challenge.
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SmokeGS-R: Physics-Guided Pseudo-Clean 3DGS for Real-World Multi-View Smoke Restoration
SmokeGS-R uses refined dark channel prior for pseudo-clean supervision to train 3DGS geometry, followed by ensemble-based appearance harmonization, achieving PSNR 15.21 and outperforming baselines on smoke restoration challenge data.
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GenSmoke-GS: A Multi-Stage Method for Novel View Synthesis from Smoke-Degraded Images Using a Generative Model
A multi-stage pipeline of restoration, dehazing, MLLM enhancement and 3D Gaussian Splatting with MCMC averaging achieves first place in the NTIRE 2026 smoke-degraded novel view synthesis track.
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NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results
The NTIRE 2026 challenge reports measurable progress in 3D reconstruction pipelines that handle real-world low-light and smoke degradation via the RealX3D benchmark.