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

2021 ◽  
Author(s):  
J. Raja ◽  
K. Moorthi ◽  
R. Pitchai
Keyword(s):  
Neural Network ◽  
Impulse Noise ◽  
High Density ◽  
Gray Wolf ◽  
Transform Domain ◽  
Pulse Coupled Neural Network

scholarly journals An Improved Pulse-Coupled Neural Network Model for Pansharpening

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2764 ◽  
Author(s):  
Xiaojun Li ◽  
Haowen Yan ◽  
Weiying Xie ◽  
Lu Kang ◽  
Yi Tian
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Multispectral Image ◽  
Transform Domain ◽  
Human Visual Perception ◽  
Pulse Coupled Neural Network ◽  
Proposed Model ◽  
Spectral Fidelity ◽  
Fusion Methods ◽  
Injection Mechanism

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

scholarly journals Liver Lesions Diagnosis Using Gwo-Anfis Framework

2019 ◽  
Vol 8 (6) ◽  
pp. 3692-3696
Keyword(s):  
Neural Network ◽  
Impulse Noise ◽  
Fuzzy Inference ◽  
Texture Feature ◽  
Noise Removal ◽  
Classification Model ◽  
Gray Wolf ◽  
Liver Lesions ◽  
Inference System ◽  
Impulse Noise Removal

In this paper, liver abnormality is detected using an improved classification model that consists of series of process. The study reveals the liver condition to be normal or abnormal using the proposed system. The study uses both structural and statistical analysis, where both these analysis is combined with the process of classification. Initially, the noises are removed using Impulse Noise Removal and then the Segmentation is carried out using Gray Wolf Optimisation (GWO) algorithm. After the segmentation, the features are extracted through Local Binary Patters (LBP) Operator and then Artificial Neural Network Fuzzy Inference System (ANFIS) classifies the liver regions as malignant or benign. Various images collected from laboratories are used in both training and testing stages. The results are validated in terms of two different texture feature extractors namely, GLCM and LBP. The result shows that the proposed classifier using GLCM classifier obtains improved classified patterns than the existing methods.

scholarly journals Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhi Qu ◽  
Yaqiong Xing ◽  
Yafei Song
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
High Frequency ◽  
Inverse Transformation ◽  
Transform Domain ◽  
Shearlet Transform ◽  
Pulse Coupled Neural Network ◽  
Suppress Noise ◽  
Traditional Image ◽  
Better Than

In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.

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