Single image desmogging using Gradient channel prior and Information gain based bilateral

Author(s):  
Jeevan Bala ◽  
Kamlesh Lakhwani
2019 ◽  
Vol 49 (12) ◽  
pp. 4276-4293 ◽  
Author(s):  
Dilbag Singh ◽  
Vijay Kumar ◽  
Manjit Kaur

VASA ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 122-128 ◽  
Author(s):  
Mandy Becker ◽  
Tom Schilling ◽  
Olga von Beckerath ◽  
Knut Kröger

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.


2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.


2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli

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.


2018 ◽  
Vol 30 (11) ◽  
pp. 2001
Author(s):  
Yongwei Miao ◽  
Xun Wang ◽  
Jiazhou Chen ◽  
Xudong Zhang ◽  
Yong-Tsui Lee

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.


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