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 2201–2210 of 3874 papers

TitleStatusHype
High-Resolution Image Synthesis with Latent Diffusion ModelsCode4
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
On Efficient Transformer-Based Image Pre-training for Low-Level VisionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network—0
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image RecognitionCode1
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity—0
Feature Distillation Interaction Weighting Network for Lightweight Image Super-ResolutionCode1
Stable Long-Term Recurrent Video Super-ResolutionCode1
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified