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.
Esrgan: En- hanced super-resolution generative adversarial networks
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
GaussianZoom enables high-fidelity extreme zoom-in 3D rendering from low-res inputs via an iterative framework combining geometry-consistent modeling, depth-based super-resolution, VLM detail synthesis, and an expandable continuous Level-of-Detail hierarchy.
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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GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
GaussianZoom enables high-fidelity extreme zoom-in 3D rendering from low-res inputs via an iterative framework combining geometry-consistent modeling, depth-based super-resolution, VLM detail synthesis, and an expandable continuous Level-of-Detail hierarchy.