Rencan Nie

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.

Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks

Abstract. In CIELab color space, we propose a remote sensing image fusion technique based on nonsubsampled shearlet transform (NSST) and pulse coupled neural network (PCNN), which aim to improve the efficiency and performance of the remote sensing image fusion by combining the excellent properties of the two methods. First, panchromatic (PAN) and multispectral (MS) are transformed into CIELab color space to get different color components. Second, PAN and L component of MS are decomposed by the NSST to obtain corresponding the low-frequency coefficients and high-frequency coefficients. Third, the low-frequency coefficients are fused by intersecting cortical model (ICM); the high-frequency coefficients are divided into several sub-blocks to calculate the average gradient (AG), and the linking strength β of PCNN model is determined by the AG, so that the parameters β can be adaptively set according to the quality of the sub-block images, then the sub-blocks image are input into PCNN to get the oscillation frequency graph (OFG), the method can get the fused high-frequency coefficients according to the OFG. Finally, the fused L component is obtained by inverse NSST, and the fused RGB color image is obtained through inverse CIELab transform. The experimental results demonstrate that the proposed method provide better effect compared with other common methods.

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.

A Regularized Locality Projection-Based Sparsity Discriminant Analysis for Face Recognition

Manifold learning and classifiers based on sparse representation are widely used in pattern recognition. Most of the conventional manifold learning methods are subjected to the choice of parameters. In this paper, we present a Regularized Locality Projection based on Sparsity Discriminant Analysis (RLPSD) method for Feature Extraction (FE) to understand the high-dimensional data such as face images. In RLPSD, firstly, we show the sparse representation of training samples by collaborative representation-based classification (CRC). Secondly, the idea of part optimization based on sparse representation is used to ensure the within-class compactness which combines with the labels of measurements and the weights of sparse presentation can be as small as possible. Finally, whole optimization can be directly obtained without the iteration of local optimization. Meanwhile, the separability information of between-class can be well discriminated by scatter matrix which is similar to Fisher linear discriminant analy...

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.

Color Image Fusion Researching Based on S-PCNN and Laplacian Pyramid

In this paper we propose an effective color image fusion algorithm based on the simplified pulse coupled neural networks S-PCNN and the Laplacian Pyramid algorithm. In the HSV color space, after regional clustering feature of H components by S-PCNN, and then achieved the fusion of the H component from each source image using Oscillation Frequency Graph. At the same time, decomposing S, H component by Laplacian Pyramid algorithm, and then using different fusion rules to fusion S, H component. Finally, inversing HSV transform to get RGB color image. The experiment indicates that the new color image fusion algorithm is more efficient both in the subjective aspect and the objective aspect than other commonly color image fusion algorithm.

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.