Single Image Superresolution via Directional Group Sparsity and Directional Features

2015 ◽  
Vol 24 (9) ◽  
pp. 2874-2888 ◽  
Author(s):  
Xiaoyan Li ◽  
Hongjie He ◽  
Ruxin Wang ◽  
Dacheng Tao
Keyword(s):  
Single Image ◽  
Group Sparsity ◽  
Directional Group

Sonography of subcutaneous tissue cannot determine causes of lower limb edema

VASA ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 122-128 ◽  
Author(s):  
Mandy Becker ◽  
Tom Schilling ◽  
Olga von Beckerath ◽  
Knut Kröger
Keyword(s):  
Clinical Diagnosis ◽  
Lower Limb ◽  
Subcutaneous Tissue ◽  
Hepatic Disease ◽  
Clinical Use ◽  
Single Image ◽  
Heart Insufficiency ◽  
Chronic Hepatic Disease ◽  
Limb Edema ◽  
Venous Disorders

Background: To clarify the clinical use of sonography for differentiation of edema we tried to answer the question whether a group of doctors can differentiate lymphedema from cardiac, hepatic or venous edema just by analysing sonographic images of the edema. Patients and methods: 38 (70 ± 12 years, 22 (58 %) females) patients with lower limb edema were recruited according the clinical diagnosis: 10 (26 %) lymphedema, 16 (42 %) heart insufficiency, 6 (16 %) venous disorders, 6 (16 %) chronic hepatic disease. Edema was depicted sonographically at the most affected leg in a standardised way at distal and proximal calf. 38 sets of images were anonymised and send to 5 experienced doctors. They were asked whether they can see criteria for lymphedema: 1. anechoic gaps, 2. horizontal gaps and 3. echoic rims. Results: Accepting an edema as lymphedema if only one doctor sees at least one of the three criteria for lymphatic edema on each single image all edema would be classified as lymphatic. Accepting lymphedema only if all doctors see at least one of the three criteria on the distal image of the same patient 80 % of the patients supposed to have lymphedema are classified as such, but also the majority of cardiac, venous and hepatic edema. Accepting lymphedema only if all doctors see all three criteria on the distal image of the same patients no edema would be classified as lymphatic. In addition we separated patients by Stemmers’ sign in those with positive and negative sign. The interpretation of the images was not different between both groups. Conclusions: Our analysis shows that it is not possible to differentiate lymphedema from other lower limb edema sonographically.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

Single Image Based Interactive Modeling for Modular Architectures Using Imaging Consistency

2018 ◽  
Vol 30 (11) ◽  
pp. 2001
Author(s):  
Yongwei Miao ◽  
Xun Wang ◽  
Jiazhou Chen ◽  
Xudong Zhang ◽  
Yong-Tsui Lee
Keyword(s):  
Single Image ◽  
Interactive Modeling ◽  
Modular Architectures

Multiple Light Source Estimation in a Single Image

2013 ◽  
Vol 32 (8) ◽  
pp. 170-182 ◽  
Author(s):  
Jorge Lopez-Moreno ◽  
Elena Garces ◽  
Sunil Hadap ◽  
Erik Reinhard ◽  
Diego Gutierrez
Keyword(s):  
Light Source ◽  
Single Image ◽  
Source Estimation ◽  
Light Source Estimation

scholarly journals Image-Based Automatic Watermeter Reading under Challenging Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 434
Author(s):  
Qingqi Hong ◽  
Yiwei Ding ◽  
Jinpeng Lin ◽  
Meihong Wang ◽  
Qingyang Wei ◽  
...  
Keyword(s):  
Smart Cities ◽  
Rapid Development ◽  
Mobile Network ◽  
Spatial Layout ◽  
Single Image ◽  
Automatic Instrument ◽  
Intelligent Sensors ◽  
Fifth Generation ◽  
Instrument Reading ◽  
Network Technologies

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

Sign in / Sign up

Export Citation Format

Share Document