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 321–330 of 3874 papers

TitleStatusHype
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified