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 1481–1490 of 3874 papers

TitleStatusHype
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-ResolutionCode1
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-ResolutionCode0
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution—0
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
SVCNet: Scribble-based Video Colorization Network with Temporal AggregationCode1
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image RestorationCode1
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified