A study of sine–cosine oscillation heterogeneous PCNN for image quantization

Yang, Zhen; Lian, Jing; Li, Shouliang; Guo, Yanan; Ma, Yide

doi:10.1007/s00500-018-03752-z

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…The fusion of the low-pass subband is based on the matching degree of the SIFT [ 29 , 30 ]. Suppose fdesc 1 ( i ) and fdesc 2 ( j ) are the SIFT descriptor from the low-pass subbands of the two images to be fused, where i ∈ (1, m ), j ∈ (1, n ), and m and n are the total number of the SIFT descriptor, respectively.…”

Section: Methodsmentioning

confidence: 99%

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

Wang

Chang

Liu

et al. 2021

Journal of Healthcare Engineering

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The traditional medical image fusion methods, such as the famous multi-scale decomposition-based methods, usually suffer from the bad sparse representations of the salient features and the low ability of the fusion rules to transfer the captured feature information. In order to deal with this problem, a medical image fusion method based on the scale invariant feature transformation (SIFT) descriptor and the deep convolutional neural network (CNN) in the shift-invariant shearlet transform (SIST) domain is proposed. Firstly, the images to be fused are decomposed into the high-pass and the low-pass coefficients. Then, the fusion of the high-pass components is implemented under the rule based on the pre-trained CNN model, which mainly consists of four steps: feature detection, initial segmentation, consistency verification, and the final fusion; the fusion of the low-pass subbands is based on the matching degree computed by the SIFT descriptor to capture the features of the low frequency components. Finally, the fusion results are obtained by inversion of the SIST. Taking the typical standard deviation, QAB/F, entropy, and mutual information as the objective measurements, the experimental results demonstrate that the detailed information without artifacts and distortions can be well preserved by the proposed method, and better quantitative performance can be also obtained.

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Section: Methodsmentioning

confidence: 99%

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

Wang

Chang

Liu

et al. 2021

Journal of Healthcare Engineering

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“…However, previous research on this method has primarily focused on medical applications while largely ignoring its applications in the field of water-drifting garbage image segmentation. For example, Guo et al [26] improved the PCNN by integrating a spiking cortical model to achieve coarse-to-fine mammography image segmentation. Yang et al [27] changed the popular simplified PCNN (SPCNN) model to an oscillating sine-cosine pulse-coupled neural network (SCHPCNN) and obtained good image quantization results.…”

Section: Related Researchmentioning

confidence: 99%

LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

2023

Water

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Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter V and auxiliary parameter P are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability.

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“…In Eqs. ( 6)- (12), UMij [n] and EMij[n] denote the internal activity and the dynamic threshold of an assigned neuron in position (i, j), respectively. YMij[n] is a compared result between UMij[n] and EMij[n-1] in the nth iteration.…”

Section: B Pna-mspcnn Modelmentioning

confidence: 99%

“…In the past few years, PCNN had been widely applied in image segmentation [6][7], image fusion [8][9], image denoising [10][11], image quantization [12][13], and image enhancement [14][15]. This is because PCNN has several main properties of biological neurons, such as automatic wave, variable thresholding, nonlinear modulation, synchronous pulse release, and capturing behavior.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

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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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A study of sine–cosine oscillation heterogeneous PCNN for image quantization

Cited by 8 publications

References 36 publications

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

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

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