Image fusion based on principal component analysis and high-pass filter

Metwalli, Mohamed R.; Nasr, Ayman H.; Allah, Osama S. Farag; El-Rabaie, El-Sayed M.

doi:10.1109/icces.2009.5383308

Cited by 47 publications

(20 citation statements)

References 4 publications

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“…This process is repeated for each band until all bands are transformed. A reverse wavelet transform is applied to the fused components to create the fused multispectral image (Metwalli et al, 2009). Generally, wavelet-based fused images produce good spectral preservation but poor spatial improvement.…”

Section: A Low-resolution Approximation Component (Ll)mentioning

confidence: 99%

Survey and analysis of various image fusion techniques for clinical CT and MRI images

Tamilselvan

Murugesan

2014

Int J Imaging Syst Tech

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The research and development of biomedical imaging techniques requires more number of image data from medical image acquisition devices, like computed tomography (CT), magnetic resonance imaging (MRI), positron emission technology, and single photon emission computed tomography. Multimodal image fusion is the process of combining information from various images to get the maximum amount of content captured by a single image acquisition device at different angles and different times or stages. This article analyses and compares the performance of different existing image fusion techniques for the clinical images in the medical field. The fusion techniques compared are simple or pixel-based fusion, pyramid-based fusion, and transform-based fusion techniques. Four set of CT and MRI images are used for the above fusion techniques. The performance of the fused results is measured with seven parameters. The experimental results show that out of seven parameters the values of four parameters, such as average difference, mean difference, root mean square error, and standard deviation are minimum and the values of remaining three parameters, such as peak signal to noise ratio, entropy, and mutual information are maximum. From the experimental results, it is clear that out of 14 fusion techniques taken for survey, image fusion using dual tree complex wavelet transform gives better fusion result for the clinical CT and MRI images. Advantages and limitations of all the techniques are discussed with their experimental results and their relevance.

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Section: A Low-resolution Approximation Component (Ll)mentioning

confidence: 99%

Survey and analysis of various image fusion techniques for clinical CT and MRI images

Tamilselvan

Murugesan

2014

Int J Imaging Syst Tech

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“…PCA with twist [6], [13] is used with the approximation coefficients. Considering the level of information carried by the approximation coefficients; these are processed with the PCA taken from the different blocks of the same image [8]. Here we have combined this approach with SiDWT.…”

Section: B Process Flowmentioning

confidence: 99%

Application of SiDWT with extended PCA for multi-focus images

Sweeti

Santhosh

Panigrahi

et al. 2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

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An Image is a representation of the information in visual form. The quality of the image acquired depends greatly on the type of image acquisition systems. Sometimes these systems fail to give the good results and we need to process the image to get the desired information. In this paper we will discuss the Multi-focus images and their fusion. A multi-focus image is the result of the inability of the acquisition system to acquire the image up to the desired field of depth. So here we are trying to find a best solution for fusion of multi-focus images considering the simplicity of algorithm with respect to different quantitative measures. In our previous work we have worked on the multimodality image fusion with image database of CT and MRI machines to combine complementary information from two images. Now in current work we are trying to extend the application of the proposed method to Multi-focus images. It will be a pixel level approach.

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“…Using image fusion has several advantages, including wider spatial and temporal coverage, decreased uncertainty, improved reliability and increased robustness of system performance [20]. Principal component analysis-based image fusion has been chosen for evaluation in this work [19].…”

Section: Methodsmentioning

confidence: 99%

Prediction of penetration in one-sided fillet welds by in-process joint gap monitoring—an experimental study

Sikström

Öberg

2017

Weld World

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The challenge to predict variations in penetration depth in one-sided fillet welds during robotized gas metal arc welding has been addressed by a pilot investigation of technical possibilities and limitations. The main cause for the variation in penetration depth is considered to be variation in joint gap size. Special attention has been paid in order to adopt the experimental conditions to conform to industrial welding conditions. The employed method uses in-process monitoring of joint gap size together with an empirical model relating penetration depth to gap size in order to predict this depth. The gap size estimates are based on image information from two cameras, one visual and one infrared. The results, that are evaluated off-line, confirm the development of a real-time method providing technical solutions that are industrially tractable. The results also pinpoint areas of further improvements towards increased robustness and reduced estimation uncertainties.

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Image fusion based on principal component analysis and high-pass filter

Cited by 47 publications

References 4 publications

Survey and analysis of various image fusion techniques for clinical CT and MRI images

Survey and analysis of various image fusion techniques for clinical CT and MRI images

Application of SiDWT with extended PCA for multi-focus images

Prediction of penetration in one-sided fillet welds by in-process joint gap monitoring—an experimental study

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