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 101–110 of 3874 papers

TitleStatusHype
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed ObservationsCode2
Generative Diffusion-based Downscaling for ClimateCode2
Latent Modulated Function for Computational Optimal Continuous Image RepresentationCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolutionCode2
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified