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 1461–1470 of 3874 papers

TitleStatusHype
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-ResolutionCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion StrategiesCode0
Improved Pothole Detection Using YOLOv7 and ESRGANCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified