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 2711–2720 of 3874 papers

TitleStatusHype
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
Progressive Generative Adversarial Networks for Medical Image Super resolution—0
Progressively Unfreezing Perceptual GAN—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Accurate super-resolution low-field brain MRI—0
Prompt-tuning latent diffusion models for inverse problems—0
Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility—0
Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified