TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.
Seesr: Towards semantics- aware real-world image super-resolution
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A dual-branch training-free ensemble fuses a hybrid attention network with a Mamba-based model via weighted combination to enhance super-resolution PSNR on DIV2K x4.
The NTIRE 2026 mobile real-world image super-resolution challenge received 16 valid submissions and overviews methods balancing image quality with mobile execution speed.
citing papers explorer
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TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.
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Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation
A dual-branch training-free ensemble fuses a hybrid attention network with a Mamba-based model via weighted combination to enhance super-resolution PSNR on DIV2K x4.
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The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
The NTIRE 2026 mobile real-world image super-resolution challenge received 16 valid submissions and overviews methods balancing image quality with mobile execution speed.