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 381–390 of 3874 papers

TitleStatusHype
Multi-Depth Branch Network for Efficient Image Super-ResolutionCode1
Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution HeadCode1
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion ModelsCode1
Frequency Estimation Using Complex-Valued Shifted Window TransformerCode1
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-ResolutionCode1
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation MethodsCode1
Learning from History: Task-agnostic Model Contrastive Learning for Image RestorationCode1
Towards Real-World Burst Image Super-Resolution: Benchmark and MethodCode1
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA EstimationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified