scholarly journals Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

2016 ◽  
Vol 141 (1) ◽  
pp. 27-32 ◽  
Author(s):  
R.M. Farouk ◽  
Mohammed Elsayed ◽  
Mohammed Aly
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Gabor Wavelet ◽  
Log Gabor Wavelet ◽  
Log Gabor

Content Based Image Retrieval Based on Log Gabor Wavelet Transform

2011 ◽  
Vol 403-408 ◽  
pp. 871-878 ◽  
Author(s):  
Megha Agarwal ◽  
Rudra Prakash Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Retrieval System ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Feature Descriptor ◽  
Gabor Wavelet Transform ◽  
Log Gabor Wavelet ◽  
Log Gabor

This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

Mean shift based log-Gabor wavelet image coding

2007 ◽  
Vol 8 (4) ◽  
pp. 620-624
Author(s):  
Ji-liang Li ◽  
Xiang-zhong Fang ◽  
Jun Hou
Keyword(s):  
Image Coding ◽  
Mean Shift ◽  
Gabor Wavelet ◽  
Wavelet Image Coding ◽  
Log Gabor Wavelet ◽  
Wavelet Image ◽  
Log Gabor

Image Displacement Detection under Low Illumination Using Joint Transform Correlator with Wavelet Denoising

2011 ◽  
Vol 128-129 ◽  
pp. 602-606
Author(s):  
Qi Li ◽  
Peng Ge ◽  
Hua Jun Feng ◽  
Zhi Hai Xu
Keyword(s):  
Detection Method ◽  
Wavelet Denoising ◽  
Gabor Wavelet ◽  
Joint Transform Correlator ◽  
Target Image ◽  
Low Contrast ◽  
Image Displacement ◽  
Low Illumination ◽  
Log Gabor Wavelet ◽  
Log Gabor

Since joint transform correlator (JTC) cannot directly detect the displacement between reference and target images without adequate exposure, an image displacement detection method using JTC based on log-Gabor wavelet denoising is proposed. The method uses a log-Gabor wavelet transform to denoise the reference and the target image obtained in the condition lack of enough exposure, preserving the phase information of them. Results show that the method can successfully accomplish the motion detection, RMSE of displacement measurement using JTC with wavelet denoising could be within 0.3 pixels under 1/80 of normal exposure. The method improved the detection ability of JTC in the condition of low illumination and low contrast, and has great application prospect under these circumstances.

Automatic Facial Aging/Rejuvenating Synthesis Method

2013 ◽  
Vol 284-287 ◽  
pp. 3035-3039
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu ◽  
Chun Wei Pan
Keyword(s):  
Synthesis Method ◽  
Gabor Wavelet ◽  
Facial Aging ◽  
Shape Model ◽  
Geometric Invariance ◽  
Different Ages ◽  
Log Gabor Wavelet ◽  
Human Image ◽  
Facial Images ◽  
Log Gabor

At present, the synthesizing faces of different ages does not emphasize on feature alignment and rectification of twisted images. If these situations do happen, they might cause failure and inaccuracy on synthesizing images. In this paper, we propose a reversible human facial aging/rejuvenating synthesis system which is implemented by Active Shape Model (ASM) integrated with Log-Gabor Wavelet, which can be used to search for the dementia elderly. First, we use AdaBoost and ASM algorithm to extract the feature set of human face, and rectify them by the concept of facial geometric invariance. The invariant concepts are the distance between inner corners of both eyes and the distance between the nose and chin. Then, we find manually one target image which is similar to the test image from the database, and analyze age texture of this human image by Log-Gabor wavelet in order to retrieve decomposition maps. Finally, we can effectively simulate human facial images of people of different ages by controlling the number of decomposition map of images and objectively judge the results via the density of wrinkles.

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