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 3021–3030 of 3874 papers

TitleStatusHype
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors—0
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy—0
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network—0
Zoom in to the details of human-centric videos—0
Zoom to Learn, Learn to Zoom—0
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified