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 391–400 of 3874 papers

TitleStatusHype
SEAL: A Framework for Systematic Evaluation of Real-World Super-ResolutionCode1
Implicit Neural Image StitchingCode1
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and ReconstructionCode1
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
End-to-end Alternating Optimization for Real-World Blind Super ResolutionCode1
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile DeviceCode1
TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-ResolutionCode1
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-ResolutionCode1
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing FrameworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified