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 3481–3490 of 3874 papers

TitleStatusHype
Aerial Spectral Super-Resolution using Conditional Adversarial Networks—0
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy—0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network—0
Application of convolutional neural networks in image super-resolution—0
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Task-Aware Image Downscaling—0
Task Decoupled Framework for Reference-Based Super-Resolution—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified