Quan Yan¹, Yunfan Ye¹, Jing Xia¹, Zhiping Cai^1,*, Zhilin Wang², Qiang Ni³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2545-2558, 2023, DOI:10.32604/iasc.2023.029857

Abstract Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction… More >

Gunnam Suryanarayana^1,*, D. Bhavana², P. E. S. N. Krishna Prasad³, M. M. K. Narasimha Reddy¹, Md Zia Ur Rahman²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1123-1137, 2023, DOI:10.32604/cmc.2023.031227

Abstract Single image super resolution (SISR) techniques produce images of high resolution (HR) as output from input images of low resolution (LR). Motivated by the effectiveness of deep learning methods, we provide a framework based on deep learning to achieve super resolution (SR) by utilizing deep singular-residual neural network (DSRNN) in training phase. Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs. Singular value decomposition (SVD) is applied to each LR-residual image pair to decompose into subbands of low and high frequency components. Later, DSRNN is trained on these subbands through input and output… More >

Xia Zhang^1,2, Wei Lu³, Youwei Ding^1,*, Yihua Song¹, Jinyue Xia⁴

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3141-3154, 2023, DOI:10.32604/iasc.2023.027219

Abstract Machine learning tasks such as image classification need to select the features that can describe the image well. The image has individual features and common features, and they are interdependent. If only the individual features of the image are emphasized, the neural network is prone to overfitting. If only the common features of images are emphasized, neural networks will not be able to adapt to diversified learning environments. In order to better integrate individual features and common features, based on skeleton and edge individual features extraction, this paper designed a mixed feature extraction method based on resonance filtering, named resonance… More >

Wael Deabes^1,2,*, Kheir Eddine Bouazza^1,3

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6307-6326, 2022, DOI:10.32604/cmc.2022.030420

Abstract Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography (ECT) image reconstruction. However, there is still a need to make these image reconstruction results faster and of better quality. Recently, Deep Learning (DL) is flourishing and is adopted in many fields. The DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks (ANNs). An ECT image reconstruction model using DNN is proposed in this paper. The proposed model mainly uses Residual Autoencoder called (ECT_ResAE). A large-scale dataset of 320 k instances have… More >

Kavitha Chinniyan^*, Thamil Vani Samiyappan, Aishvarya Gopu, Narmatha Ramasamy

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1877-1891, 2022, DOI:10.32604/iasc.2022.027274

Abstract Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked Autoencoder model is developed which… More >

Tao Zhang¹, Shaokui Gu¹, Jinxing Niu^1,*, Yi Cao²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4603-4614, 2022, DOI:10.32604/cmc.2022.027079

Abstract Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the… More >

Afef Houimli^1,*, IssamBen Mhamed², Bechir Letaief^1,3,4, Dorra Ben-Sellem^1,3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 881-904, 2022, DOI:10.32604/cmes.2022.016705

Abstract In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising the acquired projections using the… More >

Ningbo Jing¹, Mingqiao Li¹, Lang Liu^2,*, Yutong Shen¹, Peijiao Yang¹, Xuebin Qin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 465-476, 2022, DOI:10.32604/cmes.2022.018965

Abstract During mine filling, the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion. Therefore, the visualization of the inner mine filling of the solid–liquid two-phase flow in the pipeline is important. This paper proposes a method based on capacitance tomography for the visualization of the solid–liquid distribution on the section of a filling pipe. A feedback network is used for electrical capacitance tomography reconstruction. This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.… More >

Xuebin Qin^1,*, Yutong Shen¹, Jiachen Hu¹, Mingqiao Li¹, Peijiao Yang¹, Chenchen Ji¹, Xinlong Zhu²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1699-1717, 2022, DOI:10.32604/cmes.2022.018234

Abstract Electrical capacitance tomography (ECT) has great application potential in multiphase process monitoring, and its visualization results are of great significance for studying the changes in two-phase flow in closed environments. In this paper, compressed sensing (CS) theory based on dictionary learning is introduced to the inverse problem of ECT, and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space. Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT, it is not necessary to rely on the sparsity… More >

Zhe Yu¹, Bangyong Sun^1,3,*, Di Liu², Vincent Whannou de Dravo¹, Margarita Khokhlova⁴, Siyuan Wu³

Journal of Renewable Materials, Vol.10, No.5, pp. 1381-1395, 2022, DOI:10.32604/jrm.2022.018262

Abstract In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is… More >