Modified Pulse Coupled Neural Network Research Articles

Retinex-MPCNN: A Retinex and Modified Pulse coupled Neural Network based method for low-illumination visible and infrared image fusion

To overcome detail loss problem of infrared and low-illumination visible light image fusion, this paper proposes a novel fusion framework based on Modified Pulse Coupled Neural Network (MPCNN) and Retinex theory. First, MPCNN is designed to segment original images into regions with different weights. Second, a novel Retinex-MPCNN algorithm is proposed to enhance low-illumination visible light image, details of which can be clearer. Then, a specific weighted fusion strategy based on region segmentations of MPCNN is designed to fuse the infrared and enhanced visible light images. Different from average fusion strategy, we introduce an illumination item to increase the attention for low-illumination areas, thereby preserves more details to the fusion image. Experimental results on TNO dataset demonstrate that our proposed method can generate fusion images with clear contour and structure information. Compared with existing fusion methods, our method achieves better performance both in subjective and objective assessment.

Background Subtraction Based on Modified Pulse Coupled Neural Network in Compressive Domain

Background subtraction is the most popular technique for foreground detection to automatically separate foreground objects from background in video sequences. Much research effort has been directed into exploiting different features for background modeling to improve robustness of background models. However, most features used for background modeling are local features (such as intensities, colors and textures) which may have limitations when dealing with environments with complex and diverse changes in backgrounds, since changes may not emerge only within local regions. In this paper, inspired by mature human visual system (HVS), we propose a background subtraction method based on modified pulse coupled neural network (PCNN) in compressive domain, extracting global information for background modeling by making use of global coupling characteristics of the PCNN. Enlightened by theory of compressive sensing, we first employ compressive projection matrices to reduce data dimensionality. Then, we modify the PCNN to receive multi-channel color information as external stimuli and adjust linking strength and linking range for each neuron to enhance the ability of the network in global coupling. The modified PCNN is utilized to generate output responses containing global information of scenes for background modeling. Experimental results have demonstrated that our method has excellent performance and outperforms several advanced techniques in terms of visual and quantitative evaluations.

Medical image fusion based on modified pulse coupled neural network model and kirsch operator

The medical image fusion process integrates the information of multiple source images into a single image. This fused image can provide more comprehensive information and is helpful in clinical diagnosis and treatment. In this paper, a new medical image fusion algorithm is proposed. Firstly, the original image is decomposed into a low-frequency sub-band and a series of high-frequency sub-bands by using nonsubsampled shearlet transform (NSST). For the low-frequency sub-band, kirsch operator is used to extract the directional feature maps from eight directions and novel sum-modified-Laplacian (NSML) method is used to calculate the significant information of each directional feature map, and then, combining a sigmod function and the significant information updated by gradient domain guided image filtering (GDGF), calculate the fusion weight coefficients of the directional feature maps. The fused feature map is obtained by summing the convolutions of the weight coefficients and the directional feature maps. The final fused low-frequency sub-band is obtained by the linear combination of the eight fused directional feature maps. The modified pulse coupled neural network (MPCNN) model is used to calculate the firing times of each high-frequency sub-band coefficient, and the fused high-frequency sub-bands are selected according to the firing times. Finally, the inverse NSST acts on the fused low-frequency sub-band and the fused high-frequency sub-bands to obtain the fused image. The experimental results show that the proposed medical image fusion algorithm expresses some advantages over the classical medical image fusion algorithms in objective and subjective evaluation.

Real-time multimodal transport path planning based on a pulse neural network model

A modified pulse-coupled neural network (MPCNN) model is designed for real-time collision-free path planning of multimodal transport choice in stationary or non-stationary environments. The proposed neural network is topologically organised with only local lateral connections among neurons. The optimisation networks model consists of transport distance, transport time, transit costs and transit time and other factors, and then all factors compose to weight of the networks to realise the transformation to solve the shortest path problem. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network

This paper presents a novel iterative thresholding segmentation method based on a modified pulse coupled neural network (PCNN) for partitioning pixels carefully into a corresponding cluster. In the modified model, we initially simplify the two inputs of the original PCNN, and then construct a global neural threshold instead of the original threshold under the specified condition that the neuron will keep on firing once it begins. This threshold is shown to be the cluster center of a region in which corresponding neurons fire, and which can be adaptively updated as soon as neighboring neurons are captured. We then propose a method for automatically adjusting the linking coefficient based on the minimum weighted center distance function. Through iteration, the threshold can be made to converge at the possible real center of object region, thus ensuring that the final result will be obtained automatically. Finally, experiments on several infrared images demonstrate the efficiency of our proposed model. Moreover, based on comparisons with two efficient thresholding methods, a number of PCNN-based models show that our proposed model can segment images with high performance.

A Modified Pulse Coupled Neural Network with Anisotropic Synaptic Weight Matrix for Image Edge Detection

A Modified Pulse Coupled Neural Network with Anisotropic Synaptic Weight Matrix for Image Edge Detection

Multifocus image fusion using modified pulse coupled neural network for improved image quality

The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.

Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.