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 441–450 of 3874 papers

TitleStatusHype
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion ModelCode1
Reciprocal Attention Mixing Transformer for Lightweight Image RestorationCode1
mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectraCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image SuperresolutionCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified