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 1951–1960 of 3874 papers

TitleStatusHype
Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe—0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
RGB-Guided Resolution Enhancement of IR Images—0
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels—0
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
Panoramas from Photons—0
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified