A New Algorithm of Image Segmentation Based on Bidirectional Search Pulse-Coupled Neural Network

In this paper, a review of parameter optimization methods of pulse-coupled neural networks (PCNNs) is presented. Considering that PCNN has been used in image processing for many years, the aim of this paper was to provide an overview of the work that has been done and to serve as a useful reference for those who are looking for PCNN parameter optimization methods and those who are researching PCNN applications for a specific field. This paper first briefly reviews the PCNN model, including the standard PCNN and several variants of PCNN. Then, we emphasize the optimization methods for PCNN’s parameters, describing three types of parameter optimization methods in detail. Next, the paper summarizes the applications of the optimized models of PCNN with adaptive parameters in image segmentation, image fusion, image denoising and edge detection.

Image segmentation is a very important aspect in the field of image processing. According to certain principles, it will be divided into a number of small regional disjoint. Small region is a sense sets of common attributes of the connectivity pixels. The so-called connectivity which refers to the fact that any two points in the pool existing a connectivity path between all belong to the set. Pulse coupled neural networks was proposed based on the brain simultaneously pulsed release phenomenon of the visual cortex of the cats, monkeys, and other animals, the similar input data can be outbreak synchronized through interrelated neurons. The digital image can be processed by pulse coupled neural networks, the pixels of space based approach and brightness can be made up to a group. Pulse coupled neural networks has a good nature in image processing applications, which can effectively for the various aspects of image processing, and it can be used in the image processing system. Experimental results show that the method can achieve better image segmentation and have a common applicability.

The method by which data from multiple images is incorporated into a single image in order to enhance the quality of the image and reduce the artifacts, randomness and redundancy is known as image fusion. Image fusion plays a vital role in medical diagnosis and treatment. In this paper, a new image fusion algorithm using pulse-coupled neural network (PCNN) with genetic algorithm (GA) optimization has been proposed. Sixteen different sets of CT and PET images have been utilized to validate the performance of the proposed technique. Initially, PCNN has been applied to N layers of the image, and then, fusion coefficient is figured out of Layer N by using genetic algorithm (GA). The fused image obtained contains both functional and anatomical information which is present in individual CT and PET images. The proposed algorithm has been compared with pulse-coupled neural network (PCNN) via subjective and objective analyses. Experimental results illustrate the effectiveness of the proposed algorithm than the existing image fusion techniques.

Image Fusion Algorithm Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

In order to overcome the lacking of Shift invariance in Contourlet Transform, enable the image fusion to be in accord with human vision properties, Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks(PCNN) were used jointly in image fusion algorithms. Original images were decomposed to get the coefficients of low frequency sub bands and high frequency sub bands. The coefficients of low and high frequency sub bands were processed by a modified PCNN. Matching degree of original images is defined and used in fusion rules. Fusion image was obtained by NSCT inverse transformation. Experimental result shows this method is better than Wavelet, Contourlet and traditional PCNN methods, it has bigger mutual information, so the fusion image include more original image's information.

Image fusion combines information from multiple images of the same scene to obtain a composite image which is more suitable for further image processing tasks. This study presented an image fusion scheme based on the modified dual pulse coupled neural network (PCNN) in non-subsampled contourlet transform (NSCT) domain. NSCT can overcome the lack of shift invariance in contourlet transform. Original images were decomposed to obtain the coefficients of low-frequency subbands and high-frequency subbands. In this fusion scheme, a new sum-modified Laplacian of the low-frequency subband image, which represents the edge-feature of the low-frequency subband image in NSCT domain, is presented and input to motivate modified dual PCNN. For fusion of high-frequency subband coefficients, spatial frequency will be used as the gradient features of images to motivate dual channel PCNN and to overcome Gibbs phenomena. Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.

Image analysis for airborne reconnaissance and missile applications

Pulse coupled neural network (PCNN) is a new neural network model known as the third generation of artificial neural network. It was proposed through the study of the mammalian visual cortex pulse oscillation phenomenon, so PCNN has its biological basis. Over the past 20 years, PCNN has been a hot research field in digital image processing for its unique advantages. And there were also many achievements in other fields of the intelligent information processing. In this paper, we make a review of PCNN latest research progress about the algorithms theory and applications. It describes the basic pulse coupled neural network theory and models firstly, then analyse its features. Next you will see some improved network models and new applications of PCNN raised by researchers in recent years. Finally it pointed out the research and development prospects of PCNN in the future.

It needs set parameters on image segmentation based on PCNN (Pulse Coupled Neural Network) now. This paper points out the new method for medical image segmentation based on improved PCNN and Tsallis entropy. The new methods can automatically segment the medical images without selecting the PCNN parameters. It gets the best results with combining with the Tsallis entropy. The new method is very useful for PCNN application in the medical images segmentation.

Existing image fusion methods based on pulse coupled neural network (PCNN) ignore the fact that the human brain is essential to the organic combination of neural network and fuzzy system. As a result, some pixels that carry detailed information cannot activate neurons, thereby resulting in loss of details of source images and lowering the quality of fused images. To solve this problem, the present study proposed an infrared and visible image fusion method based on fuzzy pulse coupled neural network (FPCNN) and salience detection. This proposed method constructed FPCNN by combining the fuzzy theory and pulse coupled neural network. The cartoon parts obtained from the decomposition of source images by the total variation model were fused based on FPCNN. Then, salience detection of coefficients of the convolution sparse representation of the texture parts was conducted. The texture parts were fused on the basis of salience detection results. Finally, the fused cartoon and texture parts were added to obtain the fused image. The effectiveness and superiority of the proposed method were verified by experiments. Results demonstrate that compared with the most advanced seven algorithms, the proposed algorithm is superior in terms of objective evaluation indexes such as information entropy, average gradient, structural similarity, and fusion performance based on gradient. Moreover, the proposed method has good visual effect. This study provides a reference for producing high-quality all-weather images in whole-scene description. Keywords: Image fusion, Pulse coupled neural network, Salience detection, Convolution sparse representation

Pulse-coupled neural network (PCNN) model is widely used in digital image processing, but it is always a difficult problem to set network parameters and determine the optimal segmentation. By analyzing the firing characteristics and network parameters setting for the non-coupled linking PCNN, we propose an improved non-coupled linking PCNN for image segmentation. The model introduce the coupling effect of neighboring neurons into the dynamic threshold subsystem, and using a combination of DNN network, manual adjusting on step length for setting the dynamic threshold initial value. When the dynamic threshold initial value is adjusted properly, the optimal segmentation for the image can be obtained. Using the proposed algorithm in image segmentation of Lena and mammographic images, the segmentation effect similar to that of the traditional model can be obtained by less iteration, and it shows faster speed and better robustness.

In this paper, an image fusion method using self-constraint pulse coupled neural network (PCNN) is proposed. A self-constraint restrictive function is introduced to PCNN neuron, so that the relation among neuron linking strength, pixel clarity and historical linking strength is adjusted adaptively. Then the pixels of original images corresponding to the fired and unfired neurons of PCNN are considered as target and background respectively, after which new fire mapping images are obtained for original images. Finally, the clear objects of original images are decided by the weighted fusion rule with the fire mapping images and merged into a new image. Experiment result indicates that the proposed method has better fusion performance than several traditional approaches.

Hybrid pixel based method for multimodal image fusion based on Integration of Pulse Coupled Neural Network (PCNN) and Genetic Algorithm (GA) using Empirical Mode Decomposition (EMD)

A new approach to object detection using image icons based on Unit-linking PCNN (Pulse Coupled Neural Network) is introduced in this paper. Unit-linking PCNN, which has been developed from PCNN exhibiting synchronous pulse bursts in cat and monkey visual cortexes, is a kind of time-space-coding SNN (Spiking Neural Network). We have used Unit-linking PCNN to produce the global image icons with translation and rotation invariance. Unit-linking PCNN image icon (namely global image icons) is the 1-dimentional time series, and is a kind of image feature extracted from the time information that Unit-linking PCNN code the 2-dimentional image into. Its translation and rotation invariance is a good property in object detection. In addition to translation, rotation invariance, the object detection approach in this paper is also independent of scale variation.

License plate segmentation is a key technology in the process of license plate location and recognition. How to realize automatic segmentation of license plate image under complex illumination conditions has been a hot issue in intelligent transportation system (ITS). This paper deals with license plate image segmentation under a variety of lighting conditions. Based on the adaptive segmentation of license plate images by the Pulse Coupled Neural Network (PCNN), the relationship between the license plate image contrast and the PCNN iteration entropy is analyzed. An adaptive segmentation algorithm for license plate image using Deep Neural Network (DNN) to select the optimal result is proposed, and the selected segmentation image is filtered by the connected domain, which lays a foundation for subsequent license plate location, character segmentation and recognition. Simulation experiments show that the proposed algorithm performs better license plate segmentation and optimal selection for license plate images under various lighting conditions.