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 2741–2750 of 3874 papers

TitleStatusHype
Binarized Neural Network for Single Image Super Resolution—0
QMambaBSR: Burst Image Super-Resolution with Query State Space Model—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
Quality analysis of DCGAN-generated mammography lesions—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified