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 1471–1480 of 3874 papers

TitleStatusHype
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super ResolutionCode1
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural RepresentationsCode1
Learning Generative Structure Prior for Blind Text Image Super-resolutionCode2
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up TablesCode1
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-ResolutionCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified