Region-based image fusion method using bidimensional empirical mode decomposition

This paper proposed a novel multiresolution image fusion approach, which is based on bidimensional empirical mode decomposition (BEMD). The BEMD image is different from the former multi-scale image fusion algorithms which need predetermined filters or wavelet functions. This letter made research on several key issues relevant to the 2D image decomposition and proposed a BEMD image fusion rule to 2D Intrinsic Mode Functions (2D IMF) and residue image of BEMD. The experiments showed the improved performance of this novel algorithm compared with the discrete wavelet transform (DWT).

The interpolation causes the frequency aliasing in intrinsic mode functions (IMF) in bi-dimensional empirical mode decomposition (BEMD). The local orthogonality or the computational complexity of the improved BEMDs can not be met. In this paper, a novel BEMD - local estimation empirical mode decomposition (LEEMD) is proposed. Particularly, the local estimation instead of the interpolation without changing the framework of EMD is proposed to solve the problem. LEEMD will be applied in image fusion based on the proposed background/detail rule. In experiments, the objective and subjective experiments show that LEEMD and its application is effective.

The image fusion plays a crucial role in many fields such as remote sensing, medical and robotics applications. This paper is focused on image fusion of images of different focus depth. The aim is to study these concepts and provide simulations and evaluations on various implementations. When performing image fusion the images are decomposed by bi-dimensional Empirical mode decomposition (BEMD) to obtain high frequency coefficients which is used to determine which parts of the input images that makes it into the fused image. The same technique is tested on images of different modality. In this thesis, a novel bi-dimensional Empirical mode decomposition (BEMD) based image fusion scheme is proposed. The BEMD decomposes the source images into intrinsic mode functions (IMFs) and residual components. IMF components of the first signal in the decomposition of the source images are used to generate the fused images using appropriate fusion rule. Performance evaluation of fused images is done by computing fusion quality metrics and the fusion results are compared with other existing fusion schemes. It is seen that the performance of the proposed scheme is better as compared with the existing fusion schemes.

The complex bidimensional empirical mode decomposition

Infrared and visible image fusion based on BEMSD and improved fuzzy set

The data interpolation is an essential part of Bidimensional Empirical Mode Decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However, only one or two of the RBF interpolators have been utilized for BEMD so far. This paper employs many RBF interpolators for BEMD, compares their performances, and finds out the useful ones for BEMD especially in the image filtering application. We propose to apply the BEMD approach with the adequate interpolation function in the image denoising domain. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. The analysis is done using real images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters of the BEMD process. The study is believed to work as a guideline in the area of BEMD based real image in the filtering application.

In this paper window empirical mode decomposition (WEMD) is proposed and is used to do texture segmentation. Empirical mode decomposition (EMD) can decompose the nonstationary and nonlinear signals by sifting into a few intrinsic mode functions (IMFs) which represent a simple oscillatory mode of local data. However, the traditional bidimensional EMD (BEMD) has two drawbacks of the gray spots in IMF image and the slow computation speed. WEMD can solve such problems. Based on the characteristic of WEMD and local time/space-frequency analysis of structure multivector, the renovate technique of texture segmentation is also presented. Characterized by the local amplitude and the local frequency of every IMF component, the texture image can be segmented by k-means clustering algorithm. The subsequent experimental results indicate this method's effectiveness.

Primal sketch performs an important role in early vision. In this paper, we propose a novel method to obtain the primal sketch of natural images by integrating empirical mode decomposition (EMD) techniques and image gradient. 2D EMD approach can decompose the image into a finite number of intrinsic mode functions (IMF), and each one represents the original image in a different scale, with the 1st IMF representing the finest scale. To enhance the information represented by the IMF, we multiply the 1st IMF by the image gradient. This enhanced IMF highlights intensity changes in the image. By linking all the maximal points in the enhanced IMF, we obtain a primal sketch of the original image. Compared with the existed primal sketch extraction methods, our method is fully driven by the image data, and it needs neither to choose filters nor to learn the image bases. The experiment results show that our method is fast and effective.

Fabric defect detection has attracted increasing attention in the fields of computer vision and textile engineering because it is essential to quality assurance of textile manufacturing. In this paper, we propose a novel defect detection scheme for fabric inspection based on bidimensional empirical mode decomposition. The stopping criterion for sifting and the intrinsic mode functions (IMFs) are adapted for this specific application. Appropriate IMFs are selected to eliminate influences of fabric textures and lighting in defect segmentation. The experiment results on sample images from our laboratory and from TILDA’s Textile Texture Database demonstrate that the proposed method is a robust and accurate approach for fabric defect inspection.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

Several transformations are used in image fusion to extract the different image spatial details. In this paper, an improved version of the BEMD (Bi-dimensional Empirical Mode Decomposition) is proposed and is used to propose an image fusion method aiming to have the high spatial resolution of the panchromatic image PAN and the spectral resolution of the multispectral image MS in the same fused image. The proposed improved BEMD permits to avoid using injection models that are used in image fusion for preserving the spectral signatures in the fused image. Mainly, we propose a new 2D extrema points extraction for BEMD. One of the most important characteristics of the proposed BEMD components is that they are more faithful to the EMD components; their local behaviour being of pure oscillating 2D mono-components with zero mean values. Comparisons, among predecessor methods with qualitative evaluation and various spectral and spatial quantitative measures, show the effectiveness and efficiency of the proposed image fusion method. In addition, this fusion method is computationally fast and can be used to quickly merge a massive volume of data.

This paper presents a new simple and robust texture analysis feature based on Bidimensional Empirical Mode Decomposition (BEMD) and Local Binary Pattern (LBP). BEMD is a locally adaptive decomposition method and suitable for the analysis of nonlinear or nonstationary signals. Texture images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present a new set multi-scale compo- nents of images. In our approach, firstly, saddle points are added as supporting points for interpolation to improve original BEMD, and then images are decomposed by the new BEMD to several components (BIMFs). After then, Local Binary Pattern (LBP) in different sizes is used to detect features from different BIMFs. At last, normalization and BIMFs selection method are adopted for features selection. The proposed feature presents invariant while preserving LBP's simplicity. Our method has also been evaluated in CuRet and KTH-TIPS2a texture image databases. It is experimentally demonstrated that the proposed feature achieves higher classification accuracy than other state-of-the- art texture representation methods, especially in small training samples condition.

In this paper, a novel approach has been proposed for fault diagnosis of internal combustion (IC) engine using Empirical Mode Decomposition (EMD) and Neural Network. Live signals from the engines were collected with and without faults by using four sensors. The vibration signals measured from the large number of faulty engines were decomposed into a number of Intrinsic Mode Functions (IMFs). Each IMF corresponds to a specific range of the frequency component embedded in the vibration signal. This paper proposes the use of EMD technique for finding IMFs. The Cumulative Mode Function (CMF) was chosen rather than IMFs since all the IMFs are not useful to reveal the vibration signal characteristics due to the effect of noise. Statistical parameters like shape factor, crest factor etc. of the envelope spectrum of CMF were investigated as an indicator for the presence of faults. These statistical parameters are used in turn for classification of faults using Neural Networks. Resilient Propagation which is a rapidly converging neural network algorithm is used for classification of faults. The accuracy obtained by using EMD-ANN technique effectively in IC engine diagnosis for various faults is more than 85% with each sensor. By using a majority voting approach 96% accuracy has been achieved in fault classification.

The performance of interpolation algorithm used in bi-dimensional empirical mode decomposition directly affects its popularization and application. Therefore, the research on interpolation algorithm is more reasonable, accurate and fast. So far, in the interpolation algorithm adopted by the bi-dimensional empirical mode decomposition, an adaptive interpolation algorithm can be proposed according to the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. The parameters involved in the interpolation process are optimized by particle swarm optimization algorithm, and the optimal parameters are obtained, which can solve the problem of low efficiency and low precision of interpolation algorithm used in bi-dimensional empirical mode decomposition. It solves the problem that the image cannot be decomposed to obtain accurate and reliable bi-dimensional intrinsic modal function, and realize the fast decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

In this paper, we introduce a robust image watermarking method based on bi-dimensional empirical mode decomposition (BEMD) against geometric distortion. Based on the characteristics of the image theory and human visual system, the proposed method makes use of orthogonal properties of empirical mode decomposition to achieve the BEMD transform on the image. The image is decomposed into a series of IMFs and residue which contain the different frequency parts of the image. So the watermark is adaptively weighed to the different positions of the middle frequency IMF region. The method makes use of the multi-scale analysis characteristics of image BEMD theory and the person's sense of vision and shows excellent advantage against shearing attack. The method could show the watermark clearly when half of the image has been cut. Experimental results show this method excellent robustness for image shearing. The watermark thus generated is invisible and performs well in StirMark test and is robust to geometrical attacks. Compared with other watermarking algorithms, this algorithm is more robust, especially against geometric distortion, while having excellent frequency properties.