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 1921–1930 of 3874 papers

TitleStatusHype
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
A Restoration Network as an Implicit Prior—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
A New Real-World Video Dataset for the Comparison of Defogging Algorithms—0
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes—0
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution—0
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis—0
Effect of structure-based training on 3D localization precision and quality—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified