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 2321–2330 of 3874 papers

TitleStatusHype
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—0
Hazy Low-Quality Satellite Video Restoration Via Learning Optimal Joint Degradation Patterns and Continuous-Scale Super-Resolution Reconstruction—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified