DreamSR uses a dual-branch MM-ControlNet with patch-level and global prompts plus a receptive-field enhancement training strategy in a diffusion transformer to reduce over-generation and improve local texture details in ultra-high-resolution super-resolution.
Lora: Low-rank adaptation of large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
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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DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer
DreamSR uses a dual-branch MM-ControlNet with patch-level and global prompts plus a receptive-field enhancement training strategy in a diffusion transformer to reduce over-generation and improve local texture details in ultra-high-resolution super-resolution.
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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.