PCNN Mechanism and its Parameter Settings

Deng, Xiangyu; Yan, Chunman; Ma, Yide

doi:10.1109/tnnls.2019.2905113

Cited by 27 publications

(16 citation statements)

References 46 publications

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“…But key setting parameters are always set to empirical values. Deng et al studied the fire extinguishing activities of PCNN and obtained good image processing effects [49]. However, the given PCNN model still needs the verifications of related experiments based on the professional dataset.…”

Section: Related Workmentioning

confidence: 99%

A Pulse-Number-Adjustable MSPCNN and Its Image Enhancement Application

et al. 2021

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Pulse-coupled neural network (PCNN) aims to control neuronal firing state automatically and complete related image processing tasks. This paper presents a pulse-number-adjustable MSPCNN model (PNA-MSPCNN) that can automatically acquire the firing times and the firing frequency of each neuron.Hereinto, synaptic weight matrix Wijkl and decay factor  will generate an interaction value to determine the final calculation result of the internal activity U. Dynamic threshold amplitude V, step function Q, and auxiliary parameter P can precisely adjust the variation ranges of the dynamic threshold E. Additionally, we propose a low-light image enhancement method based on the above PNA-MSPCNN and a modified lowlight image enhancement (LIME). The proposed LIME algorithm focuses mainly on the parameter setting method of weight matrix Wmq, which will bring further improvement of testing image contrast. Experimental results demonstrate that our proposed method achieves better low-light image enhancement performances, compared to prevalent image enhancement methods, including SSIM of 0.8725, AMBE of 0.0550, MSE of 0.0092, and PSNR of 45.7764.

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Section: Related Workmentioning

confidence: 99%

A Pulse-Number-Adjustable MSPCNN and Its Image Enhancement Application

et al. 2021

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“…Zhang et al (2020) [15] used global and local leaf margin features to classify plant species. Thyagharajan et al [16] reviewed an array of visual descriptors and classifiers used in leaf species classification.…”

Section: Introductionmentioning

confidence: 99%

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

2021

KSII TIIS

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“…There are two main paths for studying PCNNs. The first path involves studying the parameter setting problem [10], because a PCNN is a multiparameter iteration network, and the network's performance depends on settings such as the iteration numbers, the decay time constants and linking weight coefficients. Broussard was the first to attempt to set parameters adaptively by using the gradient descent method [11].…”

Section: Introductionmentioning

confidence: 99%

The Butterfly Effect in Primary Visual Cortex

Liu,

Lian,

Sprott

et al. 2021

Preprint

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Simulating and imitating the neuronal network of humans or mammals is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by neuronal conduction characteristics in the primary visual cortex of cats, pulse-coupled neural networks (PCNNs) can exhibit synchronous oscillation behavior, which can process digital images without training. However, according to the study of single cells in the cat primary visual cortex, when a neuron is stimulated by an external periodic signal, the interspike-interval (ISI) distributions represent a multimodal distribution. This phenomenon cannot be explained by all PCNN models. By analyzing the working mechanism of the PCNN, we present a novel neuron model of the primary visual cortex consisting of a continuous-coupled neural network (CCNN). Our model inherited the threshold exponential decay and synchronous pulse oscillation property of the original PCNN model, and it can exhibit chaotic behavior consistent with the testing results of cat primary visual cortex neurons. Therefore, our CCNN model is closer to real visual neural networks. For image segmentation tasks, the algorithm based on CCNN model has better performance than the state-of-art of visual cortex neural network model. The strength of our approach is that it helps neurophysiologists further understand how the primary visual cortex works and can be

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PCNN Mechanism and its Parameter Settings

Cited by 27 publications

References 46 publications

A Pulse-Number-Adjustable MSPCNN and Its Image Enhancement Application

A Pulse-Number-Adjustable MSPCNN and Its Image Enhancement Application

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

The Butterfly Effect in Primary Visual Cortex

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