Kazim Ali^*, Adnan N. Quershi

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 271-284, 2022, DOI:10.32604/iasc.2022.021296 - 26 October 2021

Abstract The current development of artificial intelligence is largely based on deep Neural Networks (DNNs). Especially in the computer vision field, DNNs now occur in everything from autonomous vehicles to safety control systems. Convolutional Neural Network (CNN) is based on DNNs mostly used in different computer vision applications, especially for image classification and object detection. The CNN model takes the photos as input and, after training, assigns it a suitable class after setting traceable parameters like weights and biases. CNN is derived from Human Brain's Part Visual Cortex and sometimes performs even better than Haman visual… More >

Wei Fang^1,2,*, Enming Gu¹, Weinan Yi¹, Weiqing Wang¹, Victor S. Sheng³

Computer Systems Science and Engineering, Vol.41, No.2, pp. 689-698, 2022, DOI:10.32604/csse.2022.020176 - 25 October 2021

Abstract With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding More >

Ajisha Mathias¹, Samiappan Dhanalakshmi^1,*, R. Kumar¹, R. Narayanamoorthi²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168 - 11 October 2021

Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in… More >

Seonjae Kim, Dongsan Jun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651 - 27 September 2021

Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. More >

Shengchun Wang¹, Peiqi Chen¹, Jingui Huang^1,*, Tsz Ho Wong²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1167-1181, 2022, DOI:10.32604/csse.2022.018911 - 24 September 2021

Abstract We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in… More >

Samia Riaz¹, Muhammad Waqas Anwar², Irfan Riaz³, Hyun-Woo Kim⁴, Yunyoung Nam^4,*, Muhammad Attique Khan⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1-17, 2022, DOI:10.32604/cmc.2022.018268 - 07 September 2021

Abstract The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some… More >

Mahmood ul Hassan^1,*, Amin Al-Awady¹, Khalid Mahmood², Shahzad Ali³, Ibrahim Algamdi¹, Muhammad Kashif Saeed⁴, Safdar Zaman⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544 - 24 August 2021

Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving… More >

Mahmood ul Hassan^1,*, Shahzad Ali², Khalid Mahmood³, Muhammad Kashif Saeed⁴, Amin Al-Awady¹, Kamran Javed⁵, Ansar Munir Shah⁶

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.018264 - 04 June 2021

Abstract Node failure in Wireless Sensor Networks (WSNs) is a fundamental problem because WSNs operate in hostile environments. The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network. To deal with such situations, a rapid recovery mechanism is required for restoring inter-node connectivity. Due to the immense importance and need for a recovery mechanism, several different approaches are proposed in the literature. However, the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration. Moreover, most of these approaches… More >

Chung-Il Kim¹, Jehyeok Rew², Yongjang Cho², Eenjun Hwang^2,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633 - 06 May 2021

Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel More >

Ghulfam Zahra¹, Muhammad Imran¹, Abdulrahman M. Qahtani^2,*, Abdulmajeed Alsufyani², Omar Almutiry³, Awais Mahmood³, Fayez Eid Alazemi⁴

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3465-3481, 2021, DOI:10.32604/cmc.2021.017454 - 06 May 2021

Abstract In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to… More >