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 2871–2880 of 3874 papers

TitleStatusHype
GlyphDiffusion: Text Generation as Image Generation—0
RepNet-VSR: Reparameterizable Architecture for High-Fidelity Video Super-Resolution—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Representing Flow Fields with Divergence-Free Kernels for Reconstruction—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Resampling and super-resolution of hexagonally sampled images using deep learning—0
A Unified Plug-and-Play Algorithm with Projected Landweber Operator for Split Convex Feasibility Problems—0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified