DPOFusion uses direct preference optimization on property-aligned and preference-controllable latent diffusion models to produce adaptive infrared-visible image fusions aligned with heterogeneous human and machine vision demands.
Dspo: Direct semantic pref- erence optimization for real-world image super-resolution
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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.
RealSR-R1 introduces VLCoT-GRPO with four rewards to add understanding and reasoning to real-world image super-resolution models.
citing papers explorer
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Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization
DPOFusion uses direct preference optimization on property-aligned and preference-controllable latent diffusion models to produce adaptive infrared-visible image fusions aligned with heterogeneous human and machine vision demands.
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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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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought
RealSR-R1 introduces VLCoT-GRPO with four rewards to add understanding and reasoning to real-world image super-resolution models.