SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 901–910 of 3874 papers

TitleStatusHype
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
CogView3: Finer and Faster Text-to-Image Generation via Relay DiffusionCode5
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
APISR: Anime Production Inspired Real-World Anime Super-ResolutionCode5
Text-guided Explorable Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified