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 1151–1160 of 3874 papers

TitleStatusHype
FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion—0
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image SegmentationCode1
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Stochastic interpolants with data-dependent couplingsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified