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 1701–1710 of 3874 papers

TitleStatusHype
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
Feature-Driven Super-Resolution for Object Detection—0
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT—0
Deep learning-based super-resolution in coherent imaging systems—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Feature-based Recognition Framework for Super-resolution Images—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified