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 1211–1220 of 3874 papers

TitleStatusHype
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments—0
Super-Resolution Surface Reconstruction from Few Low-Resolution SlicesCode0
Towards Real-World Burst Image Super-Resolution: Benchmark and MethodCode1
Panoramas from Photons—0
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks—0
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA EstimationCode1
SEAL: A Framework for Systematic Evaluation of Real-World Super-ResolutionCode1
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks—0
Implicit Neural Image StitchingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified