scholarly journals Multi-focus Image Fusion Using Hybrid Transformation

2018 ◽  
pp. 352-359
Author(s):  
Dr. Sheshang D. Degadwala ◽  
Arpana Mahajan ◽  
Dhairya Vyas ◽  
Shivam Upadhyay ◽  
Harsh S Dave
Keyword(s):  
Image Fusion ◽  
Transform Domain ◽  
The Third ◽  
Sensing Applications ◽  
Fused Image ◽  
Focus Image ◽  
Speed Up ◽  
Remote Sensing Applications ◽  
Real Time Applications ◽  
Surveillance Application

The technique of blending two images or more than two images which produces outcome as the composite fused image. The obtained fused image is the upgraded version of original images because it has all the salient information. The present applications makes majority usage of this fused image to speed up their processing tasks in their respective fields. Recent real-time applications which require image fusion are remote sensing applications, medical applications, surveillance application, photography applications etc. the broad categorization of image fusion techniques are Non-transform domain or spatial domain and Transform domain or frequency domain. This paper initiates with the introduction of image fusion. In the second section it explains the analysis of multi-focus techniques. The third section explains hybrid image fusion strategy. Further sections elaborates the taxonomy of image fusion techniques and their comparative analysis with results.

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.

A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2508-2511 ◽  
Author(s):  
Jingjing Wang ◽  
Qian Li ◽  
Zhenhong Jia ◽  
Nikola Kasabov ◽  
Jie Yang
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Focus Image ◽  
Image Fusion Method

scholarly journals Multi-focus image fusion based on block matching in 3D transform domain

2018 ◽  
Vol 29 (2) ◽  
pp. 415-428 ◽  
Author(s):  
Dongsheng YANG ◽  
◽  
Shaohai HU ◽  
Shuaiqi LIU ◽  
Xiaole MA ◽  
...  
Keyword(s):  
Image Fusion ◽  
Block Matching ◽  
Transform Domain ◽  
Focus Image

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 401-403 ◽  
pp. 1381-1384 ◽  
Author(s):  
Zi Juan Luo ◽  
Shuai Ding
Keyword(s):  
Image Fusion ◽  
Multiple Scales ◽  
Weighted Average ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Limited Depth

t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

scholarly journals Image Fusion Based Multimodal Biometric Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 3613-3617
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
The Body ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Ringing Artifacts ◽  
Fused Image ◽  
Focus Image ◽  
Multimodal Biometric Recognition ◽  
Authentic Data

Biometric Authentication is a security process that replays on the unique biological characteristics of an individual. Biometric Authentication system compare a biometric data capture to stored, confirmed authentic data in a database. It is simply the process of verifying the identity using the measurements or other unique characteristics of the body, then logging us in a service, device and so on. It is an effective way to prove identity because it can’t be replicated. Multi focus Image fusion is a process of fusing two or more images to obtain a new one. Used to reduce the problems like blocking, ringing artifacts occurs because of DCT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. The goal is classifying the images to classes of authorized and unauthorized using multi class SVM. The fingerprint image and iris image are fused together using SWT, the features are extracted from the fused image and labelled using GLCM algorithm. The testing image is then compared with trained samples and classified as authorized or unauthorized by using FFNN.

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