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 2581–2590 of 3874 papers

TitleStatusHype
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-ResolutionCode0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution—0
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network—0
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution—0
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity—0
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Kernel-aware Burst Blind Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified