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 2181–2190 of 3874 papers

TitleStatusHype
Generative adversarial network for super-resolution imaging through a fiber—0
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks—0
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution—0
Task Decoupled Framework for Reference-Based Super-Resolution—0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Learning To Zoom Inside Camera Imaging Pipeline—0
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation—0
Texture-Based Error Analysis for Image Super-Resolution—0
Learnable Lookup Table for Neural Network QuantizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified