Adaptive Pulse Coupled Neural Network Research Articles

Research on fractional wavelet transform combined with parameter adaptive PCNN for infrared and visible image fusion algorithm

Infrared and visible image fusion holds significant value across various fields due to its ability to provide complementary information. However, existing fusion algorithms often suffer from susceptibility to external physical conditions of source images, leading to inconsistent fusion quality and limited adaptive capability. In this paper, we propose a fractional wavelet transform fusion algorithm for infrared and visible images. This algorithm uses fractional wavelet domain image decomposition to more accurately locate the image structures of different scales. Initially, we perform image decomposition using a non-subsampled shearlet transform (NSST) with multi-scale and multi-directional decomposition. Subsequently, to effectively fuse detailed information, we employ discrete fractional wavelet transform (DFRWT) to decompose low-frequency subbands while selecting an appropriate p-value. High-frequency subbands are fused using a parameterized adaptive pulse-coupled neural network (PCNN) model. To validate the superiority of our algorithm, we conduct visual and quantitative comparative analyses against several existing algorithms. The results confirm that our proposed algorithm exhibits robustness against external physical conditions and surpasses these existing algorithms, making it highly applicable for infrared–visible fusion tasks.

Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.

A novel medical image fusion method based on multi-scale shearing rolling weighted guided image filter.

Medical image fusion is a crucial technology for biomedical diagnoses. However, current fusion methods struggle to balance algorithm design, visual effects, and computational efficiency. To address these challenges, we introduce a novel medical image fusion method based on the multi-scale shearing rolling weighted guided image filter (MSRWGIF). Inspired by the rolling guided filter, we construct the rolling weighted guided image filter (RWGIF) based on the weighted guided image filter. This filter offers progressive smoothing filtering of the image, generating smooth and detailed images. Then, we construct a novel image decomposition tool, MSRWGIF, by replacing non-subsampled shearlet transform's non-sampling pyramid filter with RWGIF to extract richer detailed information. In the first step of our method, we decompose the original images under MSRWGIF to obtain low-frequency subbands (LFS) and high-frequency subbands (HFS). Since LFS contain a large amount of energy-based information, we propose an improved local energy maximum (ILGM) fusion strategy. Meanwhile, HFS employ a fast and efficient parametric adaptive pulse coupled-neural network (AP-PCNN) model to combine more detailed information. Finally, the inverse MSRWGIF is utilized to generate the final fused image from fused LFS and HFS. To test the proposed method, we select multiple medical image sets for experimental simulation and confirm its advantages by combining seven high-quality representative metrics. The simplicity and efficiency of the method are compared with 11 classical fusion methods, illustrating significant improvements in the subjective and objective performance, especially for color medical image fusion.

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

In this article, an adaptive pulse-coupled neural network (PCNN) polarization image denoising method based on Grey Wolf Optimization (GWO) and Non-Subsampled Contourlet Transform(NSCT) is proposed. Different from the traditional PCNN denoising method, the captured polarization image was firstly devised by the NSCT and enforced band-decomposition to denoised by PCNN. The evaluable index of the image was used for quantitative analysis. Then, GWO is used to update PCNN inherent voltage constant and attenuation time constant and neurons connected intensity factor three model parameters, after looking for multiple optimal solutions, and then to polarized image denoising to achieve the best effect. This method not only avoids the image edge blurring caused by the traditional image denoising method, but also solves the problem that the parameters of the PCNN are difficult to accurately estimate. Hence, it is more suitable for polarization images containing noise. The experiment and the quantitative analysis of image evaluation indices showed that NSCT-GWO-PCNN effectively suppresses the noise in polarization image by reducing salt-and-pepper noise while protecting edges.

Parameter adaptive unit-linking pulse coupled neural network based MRI–PET/SPECT image fusion

Medical image fusion has many applications to healthcare that is accomplished by extracting and then combining the complementary information from multiple medical images into a single image. The pulse coupled neural network (PCNN) is greatly applied to image fusion due to its efficient coupling of surrounding neurons, but suffers from network complexity and manual parameter settings. This paper proposes a novel parameter adaptive unit-linking PCNN (PAULPCNN) model, whose parameters are automatically obtained from the external stimulus and exhibits a simpler structure than the original PCNN. The multi-scale decomposition-based methods, particularly based on the non-subsampled shearlet transform (NSST), are widely employed by researchers to perform medical image fusion due to their efficient separation of spatial details at different scales. Motivated by the advantages of the PCNN and multi-scale decomposition-based methods, a hybrid medical image fusion method based on the PAULPCNN model is introduced in the NSST domain that merges the salient complementary details from a gray-scale and the corresponding pseudo-color medical image captured at different modalities to produce a more informative image that is more useful to medical experts in computer-aided diagnosis of diseases. The proposed method first employs NSST to transform the source images into a low-pass and several high-pass sub-bands, respectively. The high-pass sub-bands are combined using the firing times of the proposed PAULPCNN model, whereas a new distance-weighted regional energy-based rule is applied to construct the fused low-pass sub-band, which while estimating regional energy, assigns weight to different neighboring pixels depending on their distance from the central pixel. Finally, inverse NSST is applied on the fused sub-bands to construct the fused image. The effectiveness of the proposed technique is shown using the fusion results of eleven state-of-the-art methods on thirty gray-scale and pseudo-color brain medical image pairs comprising mild Alzheimer’s disease, Huntington’s disease, motor neuron disease, sagittal plane, coronal plane, transaxial plane, and glioma image pairs, where eight objective metrics are considered for the quantitative assessment. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods, which even outperforms some of these methods by providing fused images with better visual quality and greater objective performance, which are more suitable for specialists to diagnose brain diseases.

Non-Subsampling Shearlet Transform Remote Sensing Image Fusion with Improved Dual-channel Adaptive Pulse Coupled Neural Network

Non-Subsampling Shearlet Transform Remote Sensing Image Fusion with Improved Dual-channel Adaptive Pulse Coupled Neural Network

De-noising of salt and pepper noise using deep learning-based alpha-guided grey wolf optimization

The real images are undermined by salt and pepper noise due to uproarious sensors or communication errors. In this paper, a hybrid SAR image noise reduction using the adaptive pulse-coupled neural network (APCNN) which is optimized by an alpha-guided grey wolf optimizer (AgGWO) in the shearlet transform domain has been proposed. The shearlet transform is utilized to decompose the input SAR image. After the completion of AgGWO optimization, PCNN filtering strategy has been utilized to supplant the noisy pixels into related information pixel components, from which a restoration of the noise reduced images can be obtained. This proposed methodology efficiently resolves the difficulties that emerge from the basic PCNN parameter determination problem. In this methodology, the noisy pixels are secluded well from the image and original noiseless pixels are reestablished well, which can prompt better conservation of edges of an image. This proposed APCNN-AgGWO method has also been compared with other existing noise reduction methods and it yields superior de-noising impact, in terms of structural similarity index measure (SSIM) of 98.46%, peak signal-to-noise ratio (PSNR) of 39.49%, and standard deviation (STD) of 38.64%.

Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Removal of High Density Impulse Noise Using Adaptive Pulse Coupled Neural Network (APCNN) with Improved Alpha Guided Gray Wolf Optimization (IAgGWO) Technique in Transform Domain

Removal of High Density Impulse Noise Using Adaptive Pulse Coupled Neural Network (APCNN) with Improved Alpha Guided Gray Wolf Optimization (IAgGWO) Technique in Transform Domain

An optimized pulse coupled neural network image de-noising method for a field-programmable gate array based polarization camera.

The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Field-Programmable Gate Array (FPGA)-based polarization camera in this paper, in which the polarization image de-noising is implemented using an adaptive PCNN improved by Gray Wolf Optimization (GWO) and Bi-Dimensional Empirical Mode Decomposition (BEMD). Unlike other artificial neural networks, PCNN does not need to be trained, but the parameters of PCNN such as the exponential decay time constant, the synaptic junction strength factor, and the inherent voltage constant play a critical influence on its de-noising performance. GWO is able to start optimization by generating a set of random solutions as the first population and saves the optimized solutions of PCNN. In addition, BEMD can decompose a complicated image into different Bi-Dimensional Intrinsic Mode Functions with local stabilized characteristics according to the input source image, and the decomposition result is able to lower the complexity of heavy noise image analysis. Moreover, the circuit in the polarization camera is accomplished by FPGA so as to obtain the polarization image with higher quality synchronously. These two schemes are combined to attenuate different types of noises and improve the quality of the polarization image significantly. Compared with the state-of-the-art image de-noising algorithms, the noise in the polarization image is suppressed effectively by the proposed optimized image de-noising method according to the indices of peak signal-to-noise ratio, standard deviation, mutual information, structural similarity, and root mean square error.

Removal of salt and pepper noise from SAR images using optimized APCNN in Shearlet transform domain

The simultaneous elimination of salt and pepper noise and the conservation of the edge still present an unresolved challenge. In this paper, the optimized adaptive pulse-coupled neural network (APCNN) is proposed in Shearlet transform domain to reduce the salt and pepper noise present in the Synthetic Aperture Radar (SAR) images. The APCNN is optimized by Grey Wolf optimization technique. In Shearlet transform (ST) domain, PCNN upgrades the subtleties of images in the low-recurrence and high-recurrence sub-bands. Then the improved low-recurrence and high-recurrence sub-bands are utilized for inverse ST to acquire the improved images. The outcomes demonstrate that the optimized APCNN in Shearlet transform technique yields better quality of filtered SAR images. Also, the salt and pepper noise are smoother and show superior to the existing methodologies which are based on the parameter analysis of structural similarity index measure (SSIM) and peak signal-to-noise-ratio (PSNR). This method improves the PSNR value of 3.78%, and SSIM of 22.4% when compared with Adaptive PCNN denoising technique.

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.

Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network

We present a registration and fusion method of fluorescein fundus angiography image and color fundus image which combines Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Firstly, we register two images by Speeded Up Robust Features (SURF) feature points, the nearest neighbor and the next nearest neighbor distance ratio method to eliminate the spatial difference between the source images. Secondly, we use Random Sample Consensus (RANSAC) algorithm to achieve precise matching of feature points. Then, according to the transformation parameters obtained by RANSAC algorithm, we perform spatial transformation on the floating image to complete the registration. Finally, we obtain the low-frequency sub-band and high-frequency sub-band of the image to be fused by NSCT decomposition. The low-frequency sub-band is fused by the regional energy. The high-frequency sub-bands are studied using a simplified-PCNN model and the Particle Swarm Optimization algorithm. The link strength of the simplified-PCNN is an improved Laplacian energy and the images are fused based on the number of times the pixels are ignited. The proposed method has higher average gradient (AG) value and information entropy (IE) value and lower relative global dimensional synthesis error (ERGAS) than the existing fusion methods of the fundus image. The fusion image can accurately synthesize the image information, clarify the performance of the details, and has better spectral quality in the spectral range. The image of fused provides an effective reference for the clinical diagnosis of fundus diseases.

Exploiting Quality-Guided Adaptive Optimization for Fusing Multimodal Medical Images

Multimodal medical image fusion (MMIF) plays critical roles in image-guided clinical diagnostics and treatment. Pulse coupled neural network (PCNN) has been applied in image fusion for several years. In the schemes of image fusion based on PCNN, the authors have adjusted variables manually, so that it is difficult to get satisfying effects which limit in dealing with medical images with different modalities. This paper presents a quality-guided adaptive optimization method for MMIF, which is based on PCNN optimized by multi-swarm fruit fly optimization algorithm (MFOA). To reduce the implementation cost and improve the performance of the MFOA, quality assessment for multimodal medical image fusion was chosen to be the hybrid fitness function. Guided by such quality measurement, the adaptive PCNN using the MFOA (PCNN-MFOA) is proposed, which could automatically fit the optimal variables to the source images and enhance the fusion effect. The experimental results visually and quantitatively show that the proposed fusion strategy is more effective than the state-of-the-art methods and it is more effective in processing medical images with different modalities.

非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合

非下采样Contourlet变换域内结合模糊逻辑和自适应脉冲耦合神经网络的图像融合

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks.

Two systems are presented for segmentation of vertebrae in a 3D computed tomography (CT) image. The first method extracts seven features from each voxel and uses a multi-layer perceptron neural network (MLPNN) to classify the voxel as vertebrae or background. In the second method, the segmentation is completed in two steps: first, a newly developed adaptive pulse coupled neural network (APCNN) directly applied to a given image segments vertebrae, then the result is refined using a median filter. In the developed APCNN, the values for the user-defined parameters of the pulse coupled neural networks (PCNN) are adaptively adjusted for each image individually, instead of using one value for all images as in conventional PCNN. The performance of both systems in terms of Dice index (DI) was evaluated and compared against the state-of-the-art segmentation methods using seventeen clinical and standard CT images. Overall, both systems demonstrated statistically similar and promising performance with average DI > 95%. Compared to existing PCNN-based segmentation algorithms, the accuracy of the proposed APCNN improved by 29.3% on average. The developed APCNN-based system is more accurate than MLPNN-based system and existing PCNN-based algorithms in segmentation of vertebrae with blurred and weak boundaries and in the images contaminated by salt- and- pepper noise. In terms of computation time, the APCNN-based system is 16 times faster than the MLPNN-based system. Consequently, the presented APCNN-based algorithm is both accurate and fast and could be used in clinical environment for segmentation of vertebrae in 3D CT images.

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

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.

Robust Sparse Representation Combined With Adaptive PCNN for Multifocus Image Fusion

Multifocus image fusion utilizes the clear parts of multiple images from the same scene to generate a new image, which includes more information about the scene than any of the individual source images. In this paper, a multifocus image fusion method based on robust sparse representation (RSR) and an adaptive pulse-coupled neural network (PCNN) is presented. Each source image is first decomposed with RSR to obtain a sparse coefficients matrix and a residuals matrix. Second, the spatial frequency of the residuals matrix is calculated as the motivation for PCNN neurons, and a salience map of the source image is proposed as an adaptive linking strength for the PCNN. The initial decision map is acquired by comparing the ignition frequency maps of the source images obtained through PCNN. Then, the final decision map is achieved through morphological opening and closing operations. Finally, the fused image is obtained by employing a weighted fusion rule. Experimental results indicate that the proposed method is effective and better than other existing popular fusion methods regarding both objective and subjective evaluations.

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

AbstractMultimodal medical image fusion merges two medical images to produce a visual enhanced fused image, to provide more accurate comprehensive pathological information to doctors for better diagnosis and treatment. In this article, we present a perceptual multimodal medical image fusion method with free energy (FE) motivated adaptive pulse coupled neural network (PCNN) by employing Internal Generative Mechanism (IGM). First, source images are divided into predicted layers and detail layers with Bayesian prediction model. Then to retain human visual system inspired features, FE is used to motivate the PCNN for processing detail layers, and large firing times are selected as coefficients. The predicted layers are fused with the averaging strategy as activity level measurement. Finally, the fused image is reconstructed by merging coefficients in both fused layers. Experimental results visually and quantitatively show that the proposed fusion strategy is superior to the state‐of‐the‐art methods.