scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals A Critical Analysis of the Multi-Focus Images Fusion Based on Discrete Wavelet Transform and Computer Vision

2021 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel
Keyword(s):  
Computer Vision ◽  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Image Information ◽  
Depth Analysis ◽  
Fused Image ◽  
Focus Image

Abstract This paper focused on the challenge of image fusion processing and lack of reliable image information and proposed multi-focus image fusion using discrete wavelet transforms and computer vision techniques for the fused image coefficient selection process. I made an in-depth analysis and improvement on the existing algorithms from the wavelet transform and the rules of multi-focus image fusion object features’ extractions. The wavelet transform uses authentic localization properties, and computer vision provides efficient processing time and is a powerful method to analyze object focus in the high-frequency precision and steps. The process of image fusion using wavelet transformation is the wavelet basis function and wavelet decomposition level in iterative experiments to enhance fused image information. The rules of multi-focus image fusions are the wavelet transformation on the features of the high-frequency coefficients, which enhance the fusion image features reliability on the frequency domain and regional contrast of the object.

Multi-Focus Image Fusion Using Local Energy Pattern

2011 ◽  
Vol 145 ◽  
pp. 119-123
Author(s):  
Ko Chin Chang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Local Energy ◽  
View Point ◽  
Fusion Algorithm ◽  
Image Capture ◽  
Focus Image ◽  
Better Than

For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.

scholarly journals Airborne Infrared and Visible Image Fusion for Target Perception Based on Target Region Segmentation and Discrete Wavelet Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Yifeng Niu ◽  
Shengtao Xu ◽  
Lizhen Wu ◽  
Weidong Hu
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Infrared Images ◽  
Target Information ◽  
Visible Image ◽  
Region Segmentation ◽  
Target Region ◽  
Fused Image

Infrared and visible image fusion is an important precondition of realizing target perception for unmanned aerial vehicles (UAVs), then UAV can perform various given missions. Information of texture and color in visible images are abundant, while target information in infrared images is more outstanding. The conventional fusion methods are mostly based on region segmentation; as a result, the fused image for target recognition could not be actually acquired. In this paper, a novel fusion method of airborne infrared and visible image based on target region segmentation and discrete wavelet transform (DWT) is proposed, which can gain more target information and preserve more background information. The fusion experiments are done on condition that the target is unmoving and observable both in visible and infrared images, targets are moving and observable both in visible and infrared images, and the target is observable only in an infrared image. Experimental results show that the proposed method can generate better fused image for airborne target perception.

An Image Fusion Algorithm Based on Redundant Wavelet Transform

2014 ◽  
Vol 687-691 ◽  
pp. 3656-3661
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Jin Yao Yang ◽  
Li Sha Sun
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Imaging Characteristics ◽  
Focus Image ◽  
Redundant Wavelet Transform

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

scholarly journals Medical image fusion based on quaternion wavelet transform

2020 ◽  
Vol 14 ◽  
pp. 174830262093129
Author(s):  
Zhang Zhancheng ◽  
Luo Xiaoqing ◽  
Xiong Mengyu ◽  
Wang Zhiwen ◽  
Li Kai
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Medical Image Fusion ◽  
Quaternion Wavelet Transform ◽  
Fused Image

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the low-frequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

scholarly journals Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

2019 ◽  
Vol 64 (2) ◽  
pp. 211-220
Author(s):  
Sumanth Kumar Panguluri ◽  
Laavanya Mohan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Visible Image ◽  
Unsharp Masking ◽  
Fused Image ◽  
Approximation Coefficients ◽  
Performance Results

Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.

scholarly journals An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

2020 ◽  
Vol 10 (11) ◽  
pp. 3922 ◽  
Author(s):  
Guishuo Wang ◽  
Xiaoli Wang ◽  
Chen Zhao
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
High Frequency Part

The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

Multi-Focus Image Fusion Using Discrete Wavelet Transform Method

2019 ◽  
Author(s):  
Kapil Joshi ◽  
Madhu Kirola ◽  
Sumit Chaudhary ◽  
Manoj Diwakar ◽  
N.K. Joshi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Focus Image

scholarly journals Hybrid Multi-Focus Image Fusion using Stationary Wavelet Transform and Focus Measures for Visual Sensor Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3765-3769
Keyword(s):  
Wavelet Transform ◽  
Sensor Networks ◽  
Image Fusion ◽  
Discrete Wavelet ◽  
Stationary Wavelet Transform ◽  
Visual Sensor ◽  
Visual Sensor Networks ◽  
Fused Image ◽  
Fusion Methods ◽  
Focus Image

The Multifocal image fusion objective in visual sensor networks is to combine the multi-focused images of the same scene into a focused fused image with improved reliability and interpretation. However, the existing discrete wavelet-based fusion algorithms lead artifacts into the fused image due to its shift variance. But shift invariance is essential in image fusion during the reconstruction of the fused image without any loss. The Stationary Wavelet Transform is one of the most precious ones, eliminating shift variance caused by the discrete wavelet transform. And also focus measures are essential for the selection of focused objects in multi-focused images in order to get a fused image with every object in focus. Thus the advantages of Stationary wavelet transform and focus measures are considered for fusion in this paper. The proposed fusion method not only produces a focused fused image without artifacts and its performance is also good compared to other fusion methods.

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