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 211–220 of 3874 papers

TitleStatusHype
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified