Multiscale Image Fusion Using the Undecimated Wavelet Transform With Spectral Factorization and Nonorthogonal Filter Banks

Ellmauthaler, Andreas; Pagliari, Carla L.; Silva, Eduardo A. B. da

doi:10.1109/tip.2012.2226045

Cited by 79 publications

(26 citation statements)

References 35 publications

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“…Denoising the measured signals plays a vital role in effective fault diagnosis. For this purpose, Discrete Wavelet Transform (DWT) by using multiscale wavelet decomposition and reconstruction is used for signal denoising [26,27]. In this paper, DWT-based signal denoising is a necessary and mandatory step in all the studied fault diagnosis strategies.…”

Section: Dwt-based Signal Denoisingmentioning

confidence: 99%

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

et al. 2017

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Cascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applications thanks to its many advantages. However, the reliability of a CHMI is decreased with the increase of its levels. Fault diagnosis techniques play a key role in ensuring the reliability of a CHMI. The performance of a fault diagnosis method depends on the characteristics of the extracted features. In practice, some extracted features may be very similar to ensure a good diagnosis performance at some H-bridges of CHMI. The situation becomes even worse in the presence of noise. To fix these problems, in this paper, signal denoising and data preprocessing techniques are firstly developed. Then, a Principal Components Rearrangement method (PCR) is proposed to represent the different features sufficiently distinct from each other. Finally, a PCR-based fault diagnosis strategy is designed. The performance of the proposed strategy is compared with other fault diagnosis strategies, based on a 7-level CHMI hardware platform.

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Section: Dwt-based Signal Denoisingmentioning

confidence: 99%

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

et al. 2017

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“…Among pixel level fusion techniques, multi-scale transforms are the most popular technique; however, multi-scale fusion leads to distortion of images as images are derived from different sensor modalities. Andreas Ellmauthaler et al presents a different scheme to improve the performance of multi-scale image fusion known as undecimated wavelet transform based fusion scheme [6]. In this technique, the image decomposition is split into two successive filtering operations.…”

Section: Different Image Fusion Techniquesmentioning

confidence: 99%

Analysis on Image Fusion Techniques for Medical Applications

Indira¹,

Hemamalini²

2014

IJAREEIE

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ABSTRACT:Image fusion is the process of combining relevant information from a set of images into a single image in which the fused image contains more information than any of the input images. This technique improves the quality of data. Image fusion is one of the important re-processing steps in digital image reconstruction. In this paper a review on some of the image fusion techniques like curvelet transform, wavelet transform, contourlet transform, stationary wavelet transform, framelet transform etc. used in image fusion is submitted.

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“…Li et al (1995) first employed DWT for image fusion. In recent years, many pixel-level image fusion algorithms using MR transforms are proposed and successfully applied for fusion (Ellmauthaler et al, 2013).…”

Section: Recently Merging Visual and Infrared Images Is Commonlymentioning

confidence: 99%

Improving Independent Component Analysis Using Support Vector Machines for Multimodal Image Fusion

Nirmala

Paul

Vaidehi

2013

Journal of Computer Science

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The objective of this study is to combine multiple images of a scene acquired by different sensors to create a new image with all important information from the input images. Recent studies show that bases trained using Independent Component Analysis (ICA) is effective in multisensor fusion and has improved performance over traditional wavelet approaches. In the ICA based fusion, the coefficients of the input images are combined simply by selecting the coefficients with maximum magnitude. But this method resulted in fused images with poor contrast, due to the distortion introduced in constant background areas. The performance of ICA based fusion can be greatly improved by using a region based approach with intelligent decision making in order to choose the significant regions in the source images. Hence, a new region based image fusion algorithm for combining visible and Infrared (IR) images using Independent Component Analysis and Support Vector Machines (SVM) is proposed. Region based joint segmentation of the source images is carried out in the spatial domain and important features of each region are computed in spatial and transform domain. A Support Vector Machine is trained to select the regions from the source images with significant features and the corresponding ICA coefficients are combined to form the fused ICA representation. The proposed algorithm is applied to different sets of multimodal images to validate the robustness of the algorithm and compared with some standard image fusion methods. The fusion results demonstrate that the proposed scheme performs better than the state-of-the-art image fusion methods and show a significant improvement in Entropy, Petrovic and Piella evaluation metrics.

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Multiscale Image Fusion Using the Undecimated Wavelet Transform With Spectral Factorization and Nonorthogonal Filter Banks

Cited by 79 publications

References 35 publications

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

Analysis on Image Fusion Techniques for Medical Applications

Improving Independent Component Analysis Using Support Vector Machines for Multimodal Image Fusion

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