In this paper, recent pulse coupled neural networks (PCNN) model’s development, especially in the fields related to the image processing, were surveyed. Our review aims to provide a comprehensive and systematic analysis of selected researches from past few decades, having powerful methods to infer the area of study. In this paper, all selected references are categorized in three groups, on the basis of neurons structure, parameters setting, and the inherent characteristics of PCNN. Various applications of these models were mentioned and underlying difficulties, limitations, merits and disadvantages were discussed in applying these models. The researchers will find it helpful to choose and use the appropriate model for a better application.

An Overview of PCNN Model’s Development and Its Application in Image Processing

This paper presents a robust color image steganography approach for image communication over wireless communication systems. The objective of this approach is to hide three color images in one color cover image to increase the capacity of hiding as most previously published steganography approaches suffer from a capacity problem. Moreover, the investigation of wireless communication of steganography images is presented in this paper to study the sensitivity of extraction of hidden images to the channel degradation effects, which is not studied appropriately in the literature. The proposed approach depends on the Discrete Cosine and Discrete Wavelet transform. The cover image is first transformed to luminance and chrominance components for embedding the images to be hidden. The secret images are encrypted by chaotic Baker map, which is a good representative of the family of permutation-based algorithms, which tolerate the channel degradations better. The investigated wireless communication system is the Orthogonal Frequency Division Multiplexing system with channel equalization. The simulation results reveal the success of the proposed work for robust image communication.

https://link.springer.com/content/pdf/10.1007/s11277-019-06730-2.pdf

An Efficient Image Steganography Approach over Wireless Communication System

A multi-source image fusion algorithm based on gradient regularized convolution sparse representation

Automated lip reading is the process of converting movements of the lips, face and tongue to speech in real time with enhanced accuracy. Although performance of lip reading systems is still not remotely similar to audio speech recognition, recent developments in processor technology and the massive explosion and ubiquity of computing devices accompanied with increased research in this field has reduced the ambiguities of the labial language, making it possible for free speech-to-text conversion. This paper surveys the field of lip reading and provides a detailed discussion of the trade-offs between various approaches. It gives a reverse chronological topic wise listing of the developments in lip reading systems in recent years. With advancement in computer vision and pattern recognition tools, the efficacy of real time, effective conversion has increased. The major goal of this paper is to provide a comprehensive reference source for the researchers involved in lip reading, not just for the esoteric academia but all the people interested in this field regardless of particular application areas.

Lip reading techniques: A survey

The purpose of remote sensing images fusion is to produce a fused image that contains more clear, accurate and comprehensive information than any single image. an image fusion algorithm incorporating gamma-corrected is proposed based on non-subsampled Contourlet transform(NSCT). Firstly, the multispectral image is transformed to intensity-hue-saturation (IHS) system. Secondly, the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT. Then the NSCT coe?cients of high and low frequency subbands are fused by di?erent rules, respectively. For the low frequency subbands, an adaptive gamma correction was used and mutual information as the threshold for the weighted coefficient fusion. The max-abs-based fusion rule is used to fuse the high frequency coefficients. Finally, the fusion image can be obtained by performing inverse NSCT and inverse IHS transform. Experiment results demonstrate that the fused image contains abundant detailed contents and preserves the saliency structure. Our proposed method achieves better visual quality than the current methods.

A Fusion Method of Multi-spectral Image and Panchromatic Image Based on NSCT Transform and Adaptive Gamma Correction

Image fusion is the technology that combines more than one images into an image, to lay the foundation for further image processing tasks. The paper proposed a novel image fusion framework based on improved adaptive PCNN. PCNN is evolved from mammal's visual cortex neuron model, and characterized by its pulse synchronization and acquisition of the neurons. It has been proved that it is very suitable for the field of image processing, and it has been successfully applied in the field of image fusion. The two source images were input into two parallel PCNN networks, and the gray value of the image was used as the external stimuli of PCNN; At the same time, an improved Sum-modified-laplacian was selected as the image focus evaluation fuction, and linking strength of the corresponding neuron of the PCNN was calculated. The ignition map could be obtained after PCNN ignition, and the clearer part of the images were selected to generate the fused image by comparing the ignition map. In the end, the fused image was generated by pixel by pixel window-based consistency verification, and the final fusion result was obtained. Experimental results show that the proposed method is superior to the traditional image fusion methods in terms of subjective and objective evaluation criteria.

A multi-focus image fusion algorithm using modified adaptive PCNN model

This paper proposed a novel image fusion framework based on PCNN in NSCT domain. Compared with the contourlet transform(CT), NSCT has translational invariance and can also overcome the image pseudo Gibbs phenomena around singularites. PCNN is evolved from mammal's visual cortex neuron model, and characterized by its pulse synchronization and acquisition of the neurons. Firstly, the source images were decomposed into low-frequency and high-frequency sub-bands, and in the fusion process, the sum-modified-laplacian(SML) was used to motivate PCNN. In the same time, the coefficients with larger firing times were selected as the fusion coefficients. Finally, the fused image can be obtained by the inverse NSCT. Experimental results show that the method is better than the traditional fusion methods, such as the wavelet transform, the contourlet transform and the PCNN based methods both in subjective appearance and objective criteria.

A novel image fusion algorithm using PCNN In NSCT domain

Due to the characteristics of infrared and visible images, in the process of image fusion, the two kinds of heterogeneous image information will interfere with each other, which lead to the fusion image become blurred, confused and suffers contrast degradation. Therefore, a novel fusion method for infrared and visible images based on a hybrid decomposition via guided and Gaussian filters is proposed in this paper. Firstly, guided and Gaussian filters are used to achieve a hybrid decomposition for infrared and visible images, then the small-scale texture details, large-scale edges and coarse image information of the input images can be obtained. The subinformation is then merged, and the fusion weights are determined by the large-scale edges which can represent the features of the infrared image. In this way, the prominent infrared image information can be injected into the visible image. In the end, the fused image can be reconstructed by assembling the merged sub-information. Experimental results show that the proposed is better than the typical multi-scale decomposition based image fusion algorithms both in subjective and objective evaluation.

Fusion of infrared and visible images based on a hybrid decomposition via the guided and Gaussian filters

Aiming at the fusion of the infrared and visible images, a novel image fusion framework based on hybrid image decomposition and sparse representation is proposed in this paper. Firstly, the Gaussian and guided filters are used to decompose the source images into the small-scale texture details, large-scale edge and coarse-scale image information. The main infrared features are maintained in the large-scale edge information, which are used to determine the fused weights for the coarse-scale information. The sparse representation based fusion method is adopted for the fusion of the small-scale texture details and large-scale edge information, which makes the final fused image can effectively highlight the infrared targets, while preserving the texture details of the visible images as much as possible. So, the fused image is more consistent with the human visual perception effect. Experimental results show that method is superior to the currently used popular image fusion methods.

Fusion of Infrared and Visible Images through a Hybrid Image Decomposition and Sparse Representation

The current recognition method based on time-frequency analysis only uses the amplitude spectrum and ignores the phase spectrum, which leads to the recognition rate of radar signal low. In this paper, we propose an automatic recognition method based on time-frequency amplitude and phase features. For the signal time-frequency analysis, we take the short-time Fourier-based synchrosqueezing transform for radar signals to obtain two-dimensional time-frequency representation with complex values. Then, we extract the time-frequency amplitude spectrum and phase spectrum and take the absolute value of them as the two inputs of the recognition network. Next, we construct a deep convolutional network with two channels for automatic feature extraction and recognition. The simulation results on 5 kinds of polyphase codes show that the proposed method has superior performance in distinguishing the polyphase codes even at low SNR, and the phase features help to improve the recognition rate.

Polyphase-modulated radar signal recognition based on time-frequency amplitude and phase features

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