scholarly journals Multimodal Registration using the Discrete Wavelet Frame Transform

2006 ◽  
Author(s):  
Shutao Li ◽  
Jinglin Peng ◽  
J.T. Kwok ◽  
Jing Zhang
Keyword(s):  
Discrete Wavelet ◽  
Wavelet Frame ◽  
Multimodal Registration ◽  
Wavelet Frame Transform

A New Multi-Focus Image Fusion Algorithm Based on Contrast Ratio and Discrete Wavelet Frame Transform

2012 ◽  
Vol 542-543 ◽  
pp. 1011-1018
Author(s):  
Zheng Hong Deng ◽  
Mei Jing Wang ◽  
Xiao Ping Bai
Keyword(s):  
Image Fusion ◽  
Contrast Ratio ◽  
Source Image ◽  
Discrete Wavelet ◽  
Wavelet Frame ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Focus Image ◽  
Wavelet Frame Transform

This paper proposes a multi-focus image fusion algorithm based on contrast ratio and discrete wavelet frame transform. Firstly, this algorithm uses wavelet transform to perform the wavelet decomposition of the source image, and then obtains the high-frequency sub-band coefficients after the discrete wavelet frame transform to reflect the details of the image, finally, gets the fusion image obtained by wavelet reconstruction. Using evaluation indicators of information entropy, standard deviation, average gradient and spatial frequency, it objectively evaluates the fusion quality of this algorithm. The experimental results show that the quality and effect of the fusion image derived from the algorithm are significantly improved.

Pneumonia Detection Using Deep Learning Architectures

2020 ◽  
Vol 17 (12) ◽  
pp. 5535-5542
Author(s):  
Purohit Om Hemantkumar ◽  
Rakshit Lodha ◽  
Meghna Bajoria ◽  
R. Sujatha
Keyword(s):  
Feature Extraction ◽  
Imaging System ◽  
Extraction Methods ◽  
World Health ◽  
X Rays ◽  
Wavelet Frame ◽  
Training Models ◽  
Wavelet Frame Transform ◽  
Health Organization ◽  
Learning Architectures

Pneumonia is an infection caused by bacteria and viruses. It can shift from mellow to serious cases. This disease causes severe damages to the lungs since they fill with fluids. This situation causes difficulty in breathing. It further prevents oxygen to reach the blood. Pneumonia is diagnosed with the help of a chest X-rays, which can also use in the diagnosis of diseases like emphysema, lung cancer, and tuberculosis. According to WHO (World Health Organization (WHO). 2001. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children. p.4.), Chest X-rays, at present, is the best available method for detecting pneumonia. Feature extraction methods like DiscreteWavelet Transform (DWT),Wavelet Frame Transform (WFT), andWavelet Packet Transform (WPT) can be used followed by any classification algorithm. In this paper, models like Squeezenet, DenseNet, and Resnet34 have been used for image recognition. In our system, the medical images were taken from Kaggle database and were recorded using a suitable imaging system. The images retrieved were then considered for input for the system where the images go through the various phases of image processing like pre-processing, edge detection and feature extraction. Later, a variety of training models are applied to know which model offers the highest accuracy.

CONSTRUCTION OF SYMMETRIC TIGHT WAVELET FRAMES FROM QUASI-INTERPOLATORY SUBDIVISION MASKS AND THEIR APPLICATIONS

2008 ◽  
Vol 06 (01) ◽  
pp. 97-120 ◽  
Author(s):  
BYEONGSEON JEONG ◽  
MYUNGJIN CHOI ◽  
HONG OH KIM
Keyword(s):  
Filter Bank ◽  
Extension Principle ◽  
Discrete Wavelet ◽  
Wavelet Frame ◽  
Wavelet Frames ◽  
Vanishing Moments ◽  
Frequency Scale ◽  
Unitary Extension ◽  
Signal Image ◽  
Interpolatory Subdivision

This paper presents tight wavelet frames with two compactly supported symmetric generators of more than one vanishing moments in the Unitary Extension Principle. We determine all possible free tension parameters of the quasi-interpolatory subdivision masks whose corresponding refinable functions guarantee our wavelet frame. In order to reduce shift variance of the standard discrete wavelet transform, we use the three times oversampling filter bank and eventually obtain a ternary (low, middle, high) frequency scale. In applications to signal/image denoising and erasure recovery, the results demonstrate reduced shift variance and better performance of our wavelet frame than the usual wavelet systems such as Daubechies wavelets.

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