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 331–340 of 3874 papers

TitleStatusHype
Deep Blind Super-Resolution for Satellite VideoCode1
Deep Image PriorCode1
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified