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 2631–2640 of 3874 papers

TitleStatusHype
PFStorer: Personalized Face Restoration and Super-Resolution—0
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
PG-DPIR: An efficient plug-and-play method for high-count Poisson-Gaussian inverse problems—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Phase Retrieval using Untrained Neural Network Priors—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Bridging high resolution sub-cellular imaging with physiologically relevant engineered tissues—0
Photorealistic Video Generation with Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified