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 1511–1520 of 3874 papers

TitleStatusHype
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
A Study in Dataset Pruning for Image Super-Resolution—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Application of convolutional neural networks in image super-resolution—0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified