Results 1 to 10 of about 42,623 (121)

SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging [PDF]

Nature Communications
Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics.
Rui Chen, Jiasu Xu, Boqian Wang, Yi Ding, Aynur Abdulla, Yiyang Li, Lai Jiang, Xianting Ding   +7 more
doaj   +4 more sources

Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution [PDF]

Information, 2023
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan, Christian Micheloni, Niki Martinel   +2 more
doaj   +3 more sources

Enhancing fetal ultrasound image quality and anatomical plane recognition in low-resource settings using super-resolution models [PDF]

Scientific Reports
Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings.
Hafida Boumeridja, Mohammed Ammar, Mahmood Alzubaidi, Saïd Mahmoudi, Lamya Nawal Benamer, Marco Agus, Mowafa Househ, Karim Lekadir, Mostafa El Habib Daho   +8 more
doaj   +2 more sources

Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose [PDF]

BMC Medical Imaging
Objective To evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD). Methods A
Xinyan Zhou, Junyi Li, Tengfei Ke, Daoqiang Xiong, Fei Liu, Na Tan, Xirui Duan, Xiaolan Du, Feifei Zhou, Wan Shen, Rong Qian, Guochen Li, Guangrong Zheng, Lei Tang, Chengde Liao   +14 more
doaj   +2 more sources

Enhancing Historical Aerial Photographs: A New Approach Based on Non-Reference Metric and Photo Interpretation Elements [PDF]

Sensors
Deep learning-based super-resolution (SR) is an effective state-of-the-art technique for enhancing low-resolution images. This study explains a hierarchical dataset structure within the scope of enhancing grayscale historical aerial photographs with a ...
Abdullah Harun Incekara, Dursun Zafer Seker   +1 more
doaj   +2 more sources

The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

Sensors, 2022
To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution–high-resolution (LR–HR) pairs or (B) predict the degradations of an LR image and then use these to
Matthew Aquilina, Keith George Ciantar, Christian Galea, Kenneth P. Camilleri, Reuben A. Farrugia, John Abela   +5 more
doaj   +1 more source

Evaluating Deep Learning Techniques for Blind Image Super-Resolution within a High-Scale Multi-Domain Perspective

AI, 2023
Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL), they do not usually design evaluations with high scaling factors.
Valdivino Alexandre de Santiago Júnior
doaj   +1 more source

Medical image blind super‐resolution based on improved degradation process

IET Image Processing, 2023
Clinical diagnosis has high requirements for the resolution of medical images, but most existing medical images super‐ resolution (SR) methods are performed under a known or specific degradation kernel.
Dangguo Shao, Li Qin, Yan Xiang, Lei Ma, Hui Xu   +4 more
doaj   +1 more source

Cascaded Degradation-Aware Blind Super-Resolution

Sensors, 2023
Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation ...
Ding Zhang, Ni Tang, Dongxiao Zhang, Yanyun Qu   +3 more
doaj   +1 more source

Contrastive learning for a single historical painting’s blind super-resolution

Visual Informatics, 2021
Most of the existing blind super-resolution(SR) methods explicitly estimate the kernel in pixel space, which usually has a large deviation and results in poor SR performance.
Hongzhen Shi, Dan Xu, Kangjian He, Hao Zhang, Yingying Yue   +4 more
doaj   +1 more source