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 691–700 of 3874 papers

TitleStatusHype
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model InversionCode1
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
Learning Texture Transformer Network for Image Super-ResolutionCode1
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified