Shengchun Wang¹, Baoxuan Huang¹, Tsz Ho Wong², Jingui Huang^1,*, Hong Deng¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4667-4684, 2023, DOI:10.32604/cmc.2023.031444 - 28 December 2022

Abstract In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features. A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality. In order… More >

S. Girirajan, A. Pandian^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090 - 19 July 2022

Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal… More >

P. Rahul^*, B. Kaarthick

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1595-1609, 2023, DOI:10.32604/iasc.2023.025762 - 19 July 2022

Abstract In Mobile Ad Hoc Networks (MANET), Quality of Service (QoS) is an important factor that must be analysed for the showing the better performance. The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection (NQCAFFFOCHGS) has the best network performance because it uses the Improved Weighted Clustering Algorithm (IWCA) to cluster the network and the FFO algorithm, which uses fuzzy-based network metrics to select the best CH and entryway. However, the major drawback of the fuzzy system was to appropriately select the membership functions. Also, the network metrics related to… More >

Aodi Fu¹, Mingxuan Zhu², Wenzheng Yu^1,*, Xin Yao¹, Hanxiaoya Zhang³

Journal on Big Data, Vol.4, No.1, pp. 77-86, 2022, DOI:10.32604/jbd.2022.027477 - 04 May 2022

Abstract In 2014, Typhoon Rammasun invaded Qinzhou, Guangxi, causing damage to the wind tower sensor at 80 m in Qinzhou. In order to restore the wind speed at 80 m at that time, this paper was based on the hourly average wind speed data of the wind tower and meteorological station from 2017–2019, and constructed the wind speed related model of Meteorological Station and the wind measuring tower in Qinzhou, Moreover, this paper Based on the hourly average wind speed data of Qinzhou Meteorological Station in 2014, Restored the hourly average wind speed of the anemometer tower during More >

M. Nuthal Srinivasan^1,*, M. Chinnadurai²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 515-529, 2022, DOI:10.32604/csse.2022.023109 - 20 April 2022

Abstract The video inpainting process helps in several video editing and restoration processes like unwanted object removal, scratch or damage rebuilding, and retargeting. It intends to fill spatio-temporal holes with reasonable content in the video. Inspite of the recent advancements of deep learning for image inpainting, it is challenging to outspread the techniques into the videos owing to the extra time dimensions. In this view, this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network (VIA-BASDBN). The proposed VIA-BASDBN technique initially converts the videos into a set of frames and… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Ahmad Alauddin Bin Arifin², Muhammad Shahid Bhatti³, Abid Sohail³, Rohail Hassan⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.020111 - 07 December 2021

Abstract These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely More >

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 >