Kangjian He

Multifocus Color Image Fusion Based on NSST and PCNN

This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed to get the RGB color image. The experimental results indicate that the proposed color image fusion algorithm is more efficient than other common color image fusion algorithms.

Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain

A fusion algorithm based on target extraction for infrared image (IIR) and visible image fusion in the nonsubsampled contourlet transform (NSCT) domain is proposed. Commonly, the target information in IIR is important; in order to fully retain the target information in a final fused image, first, use maximum between-class variance method to segment IIR, such that the target regions with salient objects are extracted to produce the background and target images. Next, the visible and background images are decomposed to a series of low-pass and band-pass images by NSCT, respectively. Then, fuse the obtained images to produce the fused background image by different strategies in each band, in which Gaussian fuzzy logic is used to produce the low-pass coefficient; the spatial frequency of each band-pass image is used to determine the linking strength β value of pulse coupled neural network structure, and the result is used to fuse the band-pass images. Eventually, the fused image is produced combining the target image and the fused background image. The experiments show that this algorithm can retain more background details of the two images and highlight the target in the infrared image more effectively, as well as obviously improve the visual effect of the fusion image.

Multi-focus image fusion method using S-PCNN optimized by particle swarm optimization

This paper proposed a novel image fusion method based on simplified pulse-coupled neural network (S-PCNN), particle swarm optimization (PSO) and block image processing method. In general, the parameters of S-PCNN are set manually, which is complex and time-consuming and usually causes inconsistence. In this paper, the parameters of S-PCNN are set by PSO algorithm to overcome these shortcomings and improve fusion performance. Firstly, source images are divided into several equidimension sub-blocks, and then, spatial frequency is calculated as the characteristic factor of the sub-block to get the whole source image’s characterization factor matrix (CFM), and by this way the operand can be effectively reduced. Secondly, S-PCNN is used for the analysis of the CFM to get its oscillation frequency graph (OFG). Thirdly, the fused CFM will be got according to the OFG. Finally, the fused image will be reconstructed according to the fused CFM and block rule. In this process, the parameters of S-PCNN are set by PSO algorithm to get the best fusion effect. By CFM and block method, the operand of the proposed method will be effectively reduced. The experiments indicate that the multi-focus image fusion algorithm is more efficient than other traditional image fusion algorithms, and it proves that the automatically parameters setting method is effective as well.

Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.

Medical image fusion using pulse coupled neural network and multi-objective particle swarm optimization

Medical image fusion plays an important role in biomedical research and clinical diagnosis. In this paper, an efficient medical image fusion approach is presented based on pulse coupled neural network (PCNN) combining multi-objective particle swarm optimization (MOPSO), which solves the problem of PCNN parameters setting. Selecting mutual information (MI) and image quality factor (QAB/F) as the fitness function of MOPSO, the parameters of PCNN are adaptively set by the popular MOPSO algorithm. Computed tomography (CT) and magnetic resonance imaging (MRI) are the source images as experimental images. Compared with other methods, the experimental results show the superior processing performances in both subjective and objective assessment criteria.

Multi-focus: Focused Region Finding and Multi-scale Transform for Image Fusion

Abstract The purpose of image fusion is to integrate useful information from multiple images and produce a more reliable image. The key problem of multi-focus image fusion is how to determine the focused regions of the source images. As an effective and excellent fusion algorithm, the focused regions in the source images should be preserved as much as possible into the fused image. To accomplish this goal, a novel multi-focus image fusion method based on a focused regions boundary finding and multi-scale transform (MST) is proposed in this paper. The Meanshift algorithm is used to determine the focused regions first. Then, an edge detection method and morphological method are used to find the boundaries of the focused regions in the source images. For the focused boundary regions, the combination of pulse coupled neural network (PCNN) and Gaussian fuzzy method is used to produce the fused boundary region in nonsubsampled contourlet transform (NSCT) domain. Finally, the fused boundary region and the focused region of the source images are fused directly. The experimental results demonstrate that the proposed algorithm can accurately determine the focused regions, and at the same time, a better fused boundary region can be obtained; this algorithm is superior to conventional methods with respect to both objective quality evaluations and visual inspection.

A lightweight scheme for multi-focus image fusion

The aim of multi-focus image fusion is to fuse the images taken from the same scene with different focuses so that we can obtain a resultant image with all objects in focus. However, the most existing techniques in many cases cannot gain good fusion performance and acceptable complexity simultaneously. In order to improve image fusion efficiency and performance, we propose a lightweight multi-focus image fusion scheme based on Laplacian pyramid transform (LPT) and adaptive pulse coupled neural networks-local spatial frequency (PCNN-LSF), and it only needs to deal with fewer sub-images than common methods. The proposed scheme employs LPT to decompose a source image into the corresponding constituent sub-images. Spatial frequency (SF) is calculated to adjust the linking strength β of PCNN according to the gradient features of the sub-images. Then oscillation frequency graph (OFG) of the sub-images is generated by PCNN model. Local spatial frequency (LSF) of the OFG is calculated as the key step to fuse the sub-images. Incorporating LSF of the OFG into the fusion scheme (LSF of the OFG represents the information of its regional features); it can effectively describe the detailed information of the sub-images. LSF can enhance the features of OFG and makes it easy to extract high quality coefficient of the sub-image. The experiments indicate that the proposed scheme achieves good fusion effect and is more efficient than other commonly used image fusion algorithms.

A multi-focus color image fusion algorithm based on an adaptive SF-PCNN in NSCT domain

To improve the results of image fusion, taking multi-focus color image as the research object, a self-adaptive pulse coupled neural network multi-focus color image fusion method is presented, which is based on the nonsubsampled contourlet transform (NSCT). And compute the hue component and saturation component of the fused image by the fused intensity component, and finish the whole fused process of the multi-focus color image. The results show that the proposed image fusion method has a better quality than the fused by the wavelet transform and the nonsubsampled contourlet transform, as the same time the fused results in HSI model is of better quality than that fused in RGB model.

Similarity/dissimilarity calculation methods of DNA sequences: A survey

DNA sequence similarity/dissimilarity analysis is a fundamental task in computational biology, which is used to analyze the similarity of different DNA sequences for learning their evolutionary relationships. In past decades, a large number of similarity analysis methods for DNA sequence have been proposed due to the ever-growing demands. In order to learn the advances of DNA sequence similarity analysis, we make a survey and try to promote the development of this field. In this paper, we first introduce the related knowledge of DNA similarities analysis, including the data sets, similarities distance and output data. Then, we review recent algorithmic developments for DNA similarity analysis to represent a survey of the art in this field. At last, we summarize the corresponding tendencies and challenges in this research field. This survey concludes that although various DNA similarity analysis methods have been proposed, there still exist several further improvements or potential research directions in this field.

Analysis of Similarity/Dissimilarity of DNA Sequences Based on Pulse Coupled Neural Network

To calculate the similarity or dissimilarity of DNA sequences, a new method is proposed based on pulse coupled neural network (PCNN) model. First, according to the characteristics of PCNN model, we encode DNA primary sequences using a simple coding method. Then we use PCNN model to extract the entropy sequence (ES) of the encoded DNA sequence; the ES expresses the features of the DNA sequences. At last, we calculate the similarity of the ES by Euclidean distance to get the similarity of DNA sequences. We take several sets of data to test our method. The experimental results demonstrate that our method is effective.