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 1851–1860 of 3874 papers

TitleStatusHype
Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction—0
Infrared Image Super-Resolution via GAN—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation—0
Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off—0
High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network—0
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified