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 1576–1600 of 3874 papers

TitleStatusHype
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery—0
G-PCC Post-Processing Using Fractional Super-Resolution—0
Gradient-Free Adversarial Purification with Diffusion Models—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
2D Neural Fields with Learned Discontinuities—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
Fine-Grained Neural Architecture Search—0
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution—0
A statistically constrained internal method for single image super-resolution—0
A Convex Approach for Image Hallucination—0
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Guided Frequency Loss for Image Restoration—0
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified