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 3001–3010 of 3874 papers

TitleStatusHype
Structure-Preserving Super Resolution with Gradient GuidanceCode1
High-Order Residual Network for Light Field Super-ResolutionCode1
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth—0
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
ML-SIM: A deep neural network for reconstruction of structured illumination microscopy imagesCode1
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
Deep Unfolding Network for Image Super-ResolutionCode1
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector NetworkCode1
Across-scale Process Similarity based Interpolation for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified