Image fusion is the process to derive the useful information from the scene captured by infrared (IR) and visible images. This derived information is used to improve the image content by enhancing the image visualization. Human identification or any living object identification in IR images is a challenging task. This paper proposes two fusion techniques namely Discrete Wavelet Transform with Neuro-Fuzzy (NF) and Entropy (EN) (DWT-NF-EN) and Integer Wavelet Transform with Neuro-Fuzzy and Entropy (IWT-NF-EN) and their results are compared and analyzed with existing fusion techniques using different quantitative measures. Subjective and objective evaluation of the results obtained is compared with other fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Integer Wavelet Transform and Neuro-Fuzzy (IWT-NF). The objective evaluation is done using the quantitative measures Entropy (EN), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). From the experimental results it is observed that proposed methods provided better information (quality) using EN, PSNR and NCC measures for majority of the test images and the same is justified with the subjective results.

Rajkumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Mouli P.V.S.S.R.C.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Infrared and Visible Image Fusion Using Entropy and Neuro-Fuzzy Concepts

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I Advances in Intelligent Systems and Computing

Image fusion is basically a process where multiple images (more than one) are combined to form a single resultant fused image. This fused image is more productive as compared to its original input images. The fusion technique in medical images is useful for resourceful disease diagnosis purpose. This paper illustrates different multimodality medical image fusion techniques and their results assessed with various quantitative metrics. Firstly two registered images CT (anatomical information) and MRI-T2 (functional information) are taken as input. Then the fusion techniques are applied onto the input images such as Mamdani type minimum-sum-mean of maximum (MIN-SUM-MOM) and Redundancy Discrete Wavelet Transform (RDWT) and the resultant fused image is analyzed with quantitative metrics namely Over all Cross Entropy(OCE), Peak Signal-to- Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Structural Similarity Index(SSIM), Mutual Information(MI). From the derived results it is inferred that Mamdani type MIN-SUM-MOM is more productive than RDWT and also the proposed fusion techniques provide more information compared to the input images as justified by all the metrics. © 2012 IEEE.

2012 International Conference on Recent Advances in Computing and Software Systems

Medical image fusion based on redundancy DWT and Mamdani type min-sum mean-of-max techniques with quantitative analysis

Image fusion is the process of combining relevant information from two or more images into a single fused image. The resulting image will be more informative than any of the input images. The fusion in medical images is necessary for efficient diseases diagnosis from multimodality, multidimensional and multiparameter type of images. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR (pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Contourlet Transform are applied. Further the fused image is analyzed with five types of quantitative metrics such as Standard Deviation (SD), Entropy (EN), Overall Cross Entropy (OCE), Ratio of Spatial Frequency Error (RSFE), and Power Signal to Noise Ratio (PSNR) for performance evaluation. From the experimental results we observed that RDWT method provides better information (quality) using EN metric and the Contourlet Transform gives the difference in source to the fused image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics. © 2010 IEEE.

2010 3rd International Conference on Emerging Trends in Engineering and Technology

Redundancy Discrete Wavelet Transform and Contourlet Transform for Multimodality Medical Image Fusion with Quantitative Analysis

Applied Soft Computing

Infrared and visible image fusion using fuzzy logic and population-based optimization

2010 International Conference on Signal and Image Processing

Multimodal medical image fusion based on Integer Wavelet Transform and Neuro-Fuzzy

2009 Seventh International Conference on Advances in Pattern Recognition

Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform

Journal	Data powered by TypesetICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I Advances in Intelligent Systems and Computing
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	0