Xiongfei Li

Multi-focus image fusion using image-partition-based focus detection

Abstract Focus detection based fusion algorithm is a vital alternative in multi-focus image fusion applications. In this kind of fusion algorithms, focus detection measure is a key factor. However, nearly all of them tend to make incorrect predictions in the smooth regions which are close to edges and textures, because these regions are affected by edges and textures and intensities become quite different if they are blurred. In this paper, we propose a new focus detection based multi-focus image fusion algorithm. First of all, the source images are partitioned into three parts: edges, textures, and smooth regions. Pixels in smooth regions are further classified into two catalogues according to their distances from edges or textures. Then, we formulate a new focus detection rule in which pixels in smooth parts are treated differently according to their classification. Finally, the fused image is achieved with the assistance of fusing map. The interests of algorithm lie in its ability of improving the accuracy of focus detection and eliminating blockiness in fused images. Experimental results have shown that the proposed fusion algorithm retains good ratings by Human Visual System (HVS) and objective measures compared to other multi-focus fusion algorithms.

Image fusion with Internal Generative Mechanism

The Internal Generative Mechanism is brought into image fusion.A refined saliency detection method is proposed.Experiments on various images tested the effectiveness of the algorithm. In this paper, an Internal Generative Mechanism (IGM) based fusion algorithm is proposed. In the algorithm, source images are decomposed into a coarse layer and a detail layer by simulating the mechanism of human visual system perceiving images; then, the algorithm fuses the detail layer using Pulse Coupled Neural Network (PCNN), and fuses the coarse layer by using the spectral residual based saliency method; finally, coefficients in all the fused layers are combined to obtain the final fused image. The interests of the algorithm lie in the fact that it accords with the basic principles of human visual system perceiving images and it can preserve detail information that exists in source images. Experiments on various images are conducted to test the effectiveness of the algorithm. The experimental results have shown that the final images fused by the proposed algorithm achieve satisfying visual perception; meanwhile, the algorithm is superior to other traditional algorithms in terms of objective measures.

A multi-scale 3D Otsu thresholding algorithm for medical image segmentation

Abstract Thresholding technique is one of the most imperative practices to accomplish image segmentation. In this paper, a novel thresholding algorithm based on 3D Otsu and multi-scale image representation is proposed for medical image segmentation. Considering the high time complexity of 3D Otsu algorithm, an acceleration variant is invented using dimension decomposition rule. In order to reduce the effects of noises and weak edges, multi-scale image representation is brought into the segmentation algorithm. The whole segmentation algorithm is designed as an iteration procedure. In each iteration, the image is segmented by the efficient 3D Otsu, and then it is filtered by a fast local Laplacian filtering to get a smoothed image which will be input into the next iteration. Finally, the segmentation results are pooled to get a final segmentation using majority voting rules. The attractive features of the algorithm are that its segmentation results are stable, it is robust to noises and it holds for both bi-level and multi-level thresholding cases. Experiments on medical MR brain images are conducted to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed algorithm is superior to the other multilevel thresholding algorithms consistently.

Image fusion based on simultaneous empirical wavelet transform

In this paper, a new multi-scale image fusion algorithm for multi-sensor images is proposed based on Empirical Wavelet Transform (EWT). Different from traditional wavelet transform, the wavelets of EWT are not fixed, but the ones generated according to the processed signals themselves, which ensures that these wavelets are optimal for processed signals. In order to make EWT can be used in image fusion, Simultaneous Empirical Wavelet Transform (SEWT) for 1D and 2D signals are proposed, by which different signals can be projected into the same wavelet set generated according to all the signals. The fusion algorithm constructed on the 2D SEWT contains three steps: source images are decomposed into a coarse layer and a detail layer first; then, the algorithm fuses detail layers using maximum absolute values, and fuses coarse layers using the maximum global contrast selection; finally, coefficients in all the fused layers are combined to obtain the final fused image using 2D inverse SEWT. Experiments on various images are conducted to examine the performance of the proposed algorithm. The experimental results have shown that the fused images obtained by the proposed algorithm achieve satisfying visual perception; meanwhile, the algorithm is superior to other traditional algorithms in terms of objective measures.

A new multifocus image fusion based on spectrum comparison

In this paper, a spectrum comparison based multifocus image fusion algorithm is proposed. A distinctive feature of the proposed algorithm is that it constructs a global focus detection algorithm, which makes it get free of block artifacts and reduces the loss of contrast in the fused image. In this algorithm, source images are first transformed into Fourier space, in which we adopt the Bayesian prediction algorithm to smooth the log spectrum of each source image. By comparing the difference between the original log spectrum and its smoothed version, we can get the saliency region of each source image. Then image segmentation based on Sobel operator is employed to identify the smooth regions that may be affected by edges or textures, finally a sigmoid function is utilized to map the saliency comparison results to focus detection results in which affected smooth regions are treated in a different way. Experimental results demonstrate the superiority of the proposed method in terms of subjective and objective evaluation. Drawbacks of the existing image blocks selection methods are analyzed.A new focus detection method is proposed to reduce the lost of contrast.A sigmoid function is used in the fusion rule to make fused images more nature.The proposed algorithm holds for both gray-gray and color-color image fusion.

The use of ROC and AUC in the validation of objective image fusion evaluation metrics

Objective image fusion evaluation metrics play a vital role in choosing proper fusion algorithms and optimizing parameters in the field of image fusion. However, little effort has been made on their validation. In this paper, we proposed a novel validation method using ROC (Receiver Operating Characteristic) curve and AUC (the Area Under the ROC Curve). The proposed method takes the predicted quality scores into account rather than just counting how many fused images are correctly evaluated, which makes it more discriminating than other existing methods. Experimental results show that it is a reliable and precise validation method of objective fusion evaluation metrics. This paper is of particular interest to researchers focusing on objective fusion metric designing and those constructing image sets for testing objective fusion evaluation metrics. HighlightsAnalyzing drawbacks of the existing validation methods of image fusion metrics.ROC curves are adopted to validate objective image fusion evaluation metrics.The method takes the score given to each fused image into account.The method can be easily extended to other fields.

A study on improved hidden Markov models and applications to speech recognition

Visual voice lip-reading, so the computer can understand what the speakers want to express direction by looking at their lips. Lip reading is the easiest way to compare the early characters and templates from the frozen image is stored. It ignores the very nature and time changes. This method is very simple, but its just simple elements can be classified, then it may not show significant speech recognition services. Behavior was characterized by more and more common. Because of the hidden Markov model is superior (HMM), which can be widely used in speech recognition. In recent years, is also used to lip reading identification. Classical HMM model, so that the two assumptions: hidden assumptions collected: in t+1 the state can only be in this country is not in the state before t; from the hidden visible state hypothesis: only by regulating the t hide the visible state, rather than the previous state. This hypothesis is not very useful in some applications (such as lip reading) is reasonable. Under certain conditions, in the t state not only limits the t−1, but also t−2. Therefore, this study modified the assumptions of the classical HMM to derive a new HMM model and algorithms, and applied to the lip-reading recognition is increasing discrimination.

A semi-automatic brain tumor segmentation algorithm

In this paper, a novel semi-automatic segmentation algorithm is proposed to segment brain tumors from magnetic resonance imaging (MRI) images. First, an edge-aware filter is used to get the smoothed version of the original image. Secondly, Otsu based multilevel thresholding is performed on the smoothed image and the original image, respectively. Then the two segmentation maps are fused by the rule of K Nearest Neighbors (KNN) to obtain the refined segmentation result. The combination of the three steps can be denoted as multi-scale Otsu based segmentation. Finally, a bi-directional region growing method is employed to segment the brain tumor region around seeds which are inserted by the user. The proposed algorithm is tested on MRI-T2 images and it produces promising result: the segmented tumor regions are more accurate compared to those obtained by other state-of-the-art methods.

Research on a frequent maximal induced subtrees mining method based on the compression tree sequence

Save the information of the original tree in the compression tree by CTS.For each round of iterative, compression can reduce the size of the dataset.Optimize maximal stage.The proposed algorithm CFMIS mines more frequent subtrees in less time. Most complex data structures can be represented by a tree or graph structure, but tree structure mining is easier than graph structure mining. With the extensive application of semi-structured data, frequent tree pattern mining has become a hot topic. This paper proposes a compression tree sequence (CTS) to construct a compression tree model; and save the information of the original tree in the compression tree. As any subsequence of the CTS corresponds to a subtree of the original tree, it is efficient for mining subtrees. Furthermore, this paper proposes a frequent maximal induced subtrees mining method based on the compression tree sequence, CFMIS (compressed frequent maximal induced subtrees). The algorithm is primarily performed via four stages: firstly, the original data set is constructed as a compression tree model; then, a cut-edge reprocess is run for the edges in which the edge frequent is less than the threshold; next, the tree is compressed after the cut-edge based on the different frequent edge degrees; and, last, frequent subtree sets maximal processing is run such that, we can obtain the frequent maximal induced subtree set of the original data set. For each iteration, compression can reduce the size of the data set, thus, the traversal speed is faster than that of other algorithms. Experiments demonstrate that our algorithm can mine more frequent maximal induced subtrees in less time.

Lip contour extraction of RGB-based improved region growing algorithm

When one is speaking, lips change regularly. Therefore, Lip contour extraction is very meaningful. In this paper, for lip color images, this paper extracts lip contour by means of the RGB-based improved region growing algorithm. Through RGB three components, the relationship between color from and adjacent pixels is calculated. According to certain rules, seed pixels are automatically selected, and regional growth and de-noising and smoothing with median filtering are implemented. And finally, watershed algorithm is applied to get lip contour. Experimental results show that this method is simple to achieve the desired effect and good performance.