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 1301–1310 of 3874 papers

TitleStatusHype
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
Continual Learning Approaches for Anomaly DetectionCode0
Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian OperationsCode0
Handheld Multi-Frame Super-ResolutionCode0
Learning to Super Resolve Intensity Images from EventsCode0
AFN: Attentional Feedback Network based 3D Terrain Super-ResolutionCode0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified