DiSI disentangles stochastic interpolants into separate generation and regression paths, allowing controllable transitions between regression and generative image restoration with a unified few-step sampler.
Promptir: Prompting for all-in-one blind image restoration
3 Pith papers cite this work. Polarity classification is still indexing.
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TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.
Q-Agent uses CoT decomposition on a fine-tuned MLLM for multi-degradation perception plus IQA-driven greedy selection of restoration algorithms to claim better performance than All-in-One IR models.
citing papers explorer
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Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration
DiSI disentangles stochastic interpolants into separate generation and regression paths, allowing controllable transitions between regression and generative image restoration with a unified few-step sampler.
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Task-Guided Prompting for Unified Remote Sensing Image Restoration
TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.
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Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model
Q-Agent uses CoT decomposition on a fine-tuned MLLM for multi-degradation perception plus IQA-driven greedy selection of restoration algorithms to claim better performance than All-in-One IR models.