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 761–770 of 3874 papers

TitleStatusHype
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion TeacherCode1
HR-INR: Continuous Space-Time Video Super-Resolution via Event CameraCode0
Perceptual Fairness in Image Restoration—0
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution—0
HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified