siFLIM: single-image frequency-domain FLIM provides fast and photon-efficient lifetime data

2016 ◽  
Vol 13 (6) ◽  
pp. 501-504 ◽  
Author(s):  
Marcel Raspe ◽  
Katarzyna M Kedziora ◽  
Bram van den Broek ◽  
Qiaole Zhao ◽  
Sander de Jong ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Lifetime Data ◽  
Single Image ◽  
Image Frequency

An Advanced Algorithm of Multi-Focus Images Fusion Based on Wavelet Transform

2011 ◽  
Vol 187 ◽  
pp. 775-779
Author(s):  
Dong Yan Fan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Fusion Rule ◽  
High Frequencies ◽  
Focus Image ◽  
Distinct Features ◽  
Image Frequency

This paper focuses on the multi-focus image fusion based on wavelet transformation, making in-depth discussion and improvement on the existing algorithms from the aspect of the rules of multi-focus image fusion. Especially the parameter of the regional contrast is selected in the algorithm which can reflect better distinct features of the image frequency domain in the high-frequency coefficients fusion. And according to the fusion rule :" the part of the large regional contrast of wavelet high frequencies coefficients corresponding to the clear part of the image " , an improved algorithm is proposed for multi-focus image fusion based on wavelet transform regions contrast. Finally, Matlab is employed to simulation the algorithm.

New super-resolution reconstructing algorithm in image frequency domain

2009 ◽  
Vol 29 (11) ◽  
pp. 3005-3007
Author(s):  
Jin-zong LI ◽  
Xue-feng YANG ◽  
Dong-dong LI
Keyword(s):  
Frequency Domain ◽  
Super Resolution ◽  
Image Frequency

Digital image frequency spectrum method for analyzing speckle displacement in frequency domain

2015 ◽  
Vol 40 (6) ◽  
pp. 942 ◽  
Author(s):  
Hui Ge ◽  
Yuming He ◽  
Lei Shen ◽  
Dabiao Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Frequency Spectrum ◽  
Digital Image ◽  
Spectrum Method ◽  
Image Frequency

scholarly journals Comparative Analysis of Image Steganography using SLT, DCT and SLT-DCT Algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 169-182
Author(s):  
Lilik Widyawati ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Comparative Analysis ◽  
Information Security ◽  
Frequency Domain ◽  
Digital Media ◽  
Storage Capacity ◽  
Frequency Component ◽  
Image Steganography ◽  
Cover Image ◽  
Cosine Transform ◽  
Image Frequency

Steganography is an interesting science to be studied and researched at this time, because steganography is the science of hiding messages on other digital media so that other parties are not aware of the existence of information in the digital media. Steganography is very effective in maintaining information security, because the existence of this information is obscured so that it is difficult to know where it is. This paper discusses hiding text into images using the Slantlet Transform (SLT) method, Descreate Cosine Transform (DCT) and Hybrid of SLT and DCT. The three methods are implemented in the frequency domain where steganographic imagery is transformed from the spatial domain to the frequency domain and the message bit is inserted into the cover image frequency component. The comparison parameters of these three techniques are based on MSE, PSNR, Capacity & Robustness. From the results of the tests that have been done, it is obtained that the highest PSNR value is generated using the SLT-DCT method, the largest storage capacity is the SLT method while the resistance, SLT-DCT method and DCT method are more resistant to attack than the SLT method.

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.

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