Dual-channel Pulse Coupled Neural Network Research Articles

A Remote Sensing Image Fusion Method Combining Low-Level Visual Features and Parameter-Adaptive Dual-Channel Pulse-Coupled Neural Network

Remote sensing image fusion can effectively solve the inherent contradiction between spatial resolution and spectral resolution of imaging systems. At present, the fusion methods of remote sensing images based on multi-scale transform usually set fusion rules according to local feature information and pulse-coupled neural network (PCNN), but there are problems such as single local feature, as fusion rule cannot effectively extract feature information, PCNN parameter setting is complex, and spatial correlation is poor. To this end, a fusion method of remote sensing images that combines low-level visual features and a parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN) in a non-subsampled shearlet transform (NSST) domain is proposed in this paper. In the low-frequency sub-band fusion process, a low-level visual feature fusion rule is constructed by combining three local features, local phase congruency, local abrupt measure, and local energy information to enhance the extraction ability of feature information. In the process of high-frequency sub-band fusion, the structure and parameters of the dual-channel pulse-coupled neural network (DCPCNN) are optimized, including: (1) the multi-scale morphological gradient is used as an external stimulus to enhance the spatial correlation of DCPCNN; and (2) implement parameter-adaptive representation according to the difference box-counting, the Otsu threshold, and the image intensity to solve the complexity of parameter setting. Five sets of remote sensing image data of different satellite platforms and ground objects are selected for experiments. The proposed method is compared with 16 other methods and evaluated from qualitative and quantitative aspects. The experimental results show that, compared with the average value of the sub-optimal method in the five sets of data, the proposed method is optimized by 0.006, 0.009, 0.009, 0.035, 0.037, 0.042, and 0.020, respectively, in the seven evaluation indexes of information entropy, mutual information, average gradient, spatial frequency, spectral distortion, ERGAS, and visual information fidelity, indicating that the proposed method has the best fusion effect.

Parameter adaptive unit-linking dual-channel PCNN based infrared and visible image fusion

The properties like pulse synchronization of neurons and global coupling greatly motivated researchers to apply pulse coupled neural network (PCNN) models in accomplishing image fusion. However, manual adjustment of its parameters negatively affects the fusion performance. Moreover, it can process one image at a time. A new parameter adaptive unit-linking dual-channel PCNN model that exhibits all the properties of PCNN and processes two images simultaneously is used in this work to implement a novel fusion algorithm in the non-subsampled contourlet transform (NSCT) domain for the integration of infrared and visible images. At the same time, all the parameters of the proposed model are automatically estimated from the source images. The infrared and visible images are first decomposed using NSCT to provide a sequence of band-pass directional sub-bands and a low-pass sub-band, respectively. The band-pass directional sub-bands are fused using fractal dimension-based linking strength, while the low-pass sub-bands are combined using a new linking strength based on the multi-scale morphological gradient of coefficients. Lastly, the fused image is constructed from the fused sub-bands by applying inverse NSCT. Fourteen state-of-the-art methods are adopted for comparing the performance of the proposed method. The qualitative comparison is done using the human visual system, whereas six objective metrics are considered for the quantitative evaluation. The proposed method is competitive and outperforms some of the existing methods, according to the results of the experiments.

Saliency Detection Using a Bio-inspired Spiking Neural Network Driven by Local and Global Saliency

ABSTRACT The detection of the most salient parts of images as objects in salient object detection tasks mimics human behavior, which is useful for a variety of computer vision applications. In this paper, the Local and Global Saliency Driven Dual-Channel Pulse Coupled Neural Network (LGSD-DCPCNN) model is used to provide a novel strategy for saliency detection. To achieve visually homogeneous sections and save computation costs, the input image is first subjected to superpixel segmentation. The global and local saliency maps are then created using the segmented image’s position, color, and textural properties. The LGSD-DCPCNN network is activated using these saliency maps to extract visually consistent features from the input maps to provide the final saliency map. An extensive qualitative and quantitative performance study is undertaken to assess the efficacy of the proposed method. When compared to state-of-the-art approaches, the experimental results show a considerable improvement in the detection of salient regions. Quantitative analysis of the proposed method reveals a significant improvement in the area under the ROC curve (AUC) score, F-measure score, and mean absolute error (MAE) score. The qualitative analysis describes the proposed algorithm’s ability to detect multiple salient objects accurately while maintaining significant border preservation.

Fractal dimension based parameter adaptive dual channel PCNN for multi-focus image fusion

Multi-focus image fusion combines the focused parts of multiple images of the same scene to generate a fully focused image. Dual Channel Pulse coupled neural network (DCPCNN) is frequently used in image fusion framework due to its characteristics like global coupling, pulse synchronization of neurons and simultaneous processing of two images. However, it suffers from the manual setting of its parameters. This paper proposes a transform domain multi-focus image fusion method based on a novel parameter adaptive DCPCNN (PA-DCPCNN) model, in which the parameters are adaptively estimated using the inputs. Moreover, the linking strengths of the PA-DCPCNN model are computed per neuron using a new fractal dimension based focus measure (FDFM). First, the multi-scale and multi-direction decomposition of source images are performed using non-subsampled contourlet transform (NSCT) to obtain the low-pass sub-band and a number of band-pass directional sub-bands. Second, the band-pass directional sub-bands of the source images are fused using the PA-DCPCNN model. Third, the low-pass sub-bands of source images are combined using multi-scale morphological gradient (MSMG) and FDFM to generate the fused low-pass sub-band. Finally, the fused image is obtained by applying inverse NSCT to the fused sub-bands. The performance of the proposed method is compared with fourteen state-of-the-art multi-focus image fusion methods using six objective quality metrics. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods in terms of both subjective and objective assessments.

Application of deep learning model based on image definition in real-time digital image fusion

This paper focuses on pulse coupled neural network (PCNN) and digital image fusion. Aiming at the existing problems, this paper proposes a real-time deep learning model with dual-channel PCNN fusion algorithm based on image definition. It will also be helpful to digital image forensics. With the integration of the orthogonal color space that conforms to HVS, this algorithm simplifies the traditional PCNN model to a parallel dual-channel adaptive PCNN structure. Also, it can realize the adaptive processing by defining the image definition to be β, the coupled linking coefficient. As the dynamic threshold can be increased exponentially with this method, it can effectively solve the problems. The experimental result proves that our algorithm outperforms the traditional fusion algorithms according to the subjective visual effect or the objective assessment standard.

MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN

Pulse coupled neural network (PCNN) is widely used in image fusion framework due to its global coupling and pulse synchronization of neurons. However, its manual setting of parameters and inability to process multiple images affect the fusion performance. In this letter, a novel weighted parameter adaptive dual channel PCNN (WPADCPCNN) based medical fusion method is proposed in non-subsampled shearlet transform domain to fuse the magnetic resonance imaging and single-photon emission computed tomography images of AIDS dementia complex and Alzheimer's disease patients. The parameters of the proposed WPADCPCNN model are estimated from its inputs using fractal dimension. The high-pass sub-bands are fused using the WPADCPCNN model whereas the low-pass sub-bands are merged using a new weighted multi-scale morphological gradients based rule. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods in terms of both visual quality and objective assessment.

Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength

This paper presents a novel fusion framework for infrared and visual image that uses an adaptive dual-channel pulse-coupled neural network (PCNN) with triple-linking strength (ATD-PCNN) in a local non-subsampled shearlet transform (LNSST) domain. First, the LNSST, which is an upgrade based on the NSST, is used as a multi-scale analysis tool to decompose the source images into low-pass and high-pass sub-images. Second, an improved sum-modified Laplacian (ISML) that reflects the energy characteristics of low-pass sub-images in the LNSST domain is used to stimulate the ATD-PCNN model, and an improved average gradient (IAVG) that reflects the textural details of high-pass sub-images is used to stimulate the ATD-PCNN neurons. Furthermore, to solve the problem of the spectral difference between infrared and visible light, three novel operators are used as the adaptive linking strength, and an optimization model based on l2-norm is used for merging the output coefficients. A series of images from diverse scenes are used for the fusion experiments, and the fusion results are evaluated subjectively and objectively. The results of the subjective and objective evaluations show that our algorithm exhibits superior fusion performance and is more effective than typical fusion techniques.

A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain

In this paper, a novel fusion algorithm based on the adaptive dual-channel unit-linking pulse coupled neural network (PCNN) for infrared and visible images fusion in nonsubsampled contourlet transform (NSCT) domain is proposed. The flexible multi-resolution and directional expansion for images of NSCT are associated with global coupling and pulse synchronization characteristic of dual-PCNN. Compared with other dual-PCNN models, the proposed model possesses fewer parameters and is not difficult to implement adaptive, which is more suitable for image fusion. Firstly, the source images were multi-scale and multi-directional decomposed by NSCT. Then, to make dual-channel PCNN adaptive, the average gradient of each pixel was presented as the linking strength, and the time matrix was presented to determine the iteration number adaptively. In this fusion scheme, a novel sum modified-Laplacian of low-frequency subband and a modified spatial frequency of high-frequency subband were input to motivate the adaptive dual-channel unit-linking PCNN, respectively. Experimental results demonstrate that the proposed algorithm can significantly improve image fusion performance, accomplish notable target information and high contrast, simultaneously preserve rich details information, and excel other typical current methods in both objective evaluation criteria and visual effect.

Image fusion scheme based on modified dual pulse coupled neural network

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 fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain

This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT–PCNN in terms of objective criteria and visual appearance.