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Single Image Dehazing: An Analysis on Generative Adversarial Network

Journal of Computer and Communications 2020年第4期8卷 127-137页

作者： Amina Khatun mohammad reduanul haque Rabeya Basri mohammad Shorif Uddin Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh.

Haze is a very common phenomenon that degrades or reduces visibility. It causes various problems where high-quality images are required such as traffic and security monitoring. So haze removal from scenes is an immediate demand for clear vision. Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired “in the wild” and how we could gauge the progress in the field. To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN. We have experimented using benchmark dataset consisting of both synthetic and real-world hazy images. The obtained results are evaluated both quantitatively and qualitatively. Among these techniques, the DHSGAN gives the best performance.

关键词： Dehazing Deep Leaning Convulutional Neural Network (CNN) Generative Adversarial Networks (GAN)

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Image-Based Vehicle Speed Estimation

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Journal of Computer and Communications 2019年第6期7卷 1-5页

作者： Md. Golam Moazzam mohammad reduanul haque mohammad Shorif Uddin Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing accidents. Many methods, such as using RADAR and LIDAR sensors have been proposed. However, these are expensive, and their accuracy is not quite satisfactory. In this paper, a video-based vehicle speed determination method is presented. The method shows satisfactory performance on standard data sets and gives that error rate of velocity estimation is within 10%.

关键词： Vehicle Detection Speed Calculation Background Subtraction Vehicle Tracking

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Lung Cancer Detection Using CT Image Based on 3D Convolutional Neural Network

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Journal of Computer and Communications 2020年第3期8卷 35-42页

作者： Tasnim Ahmed Mst. Shahnaj Parvin mohammad reduanul haque mohammad Shorif Uddin Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Central Women’s University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh.

Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.

关键词： Lung Cancer Convolutional Neural Network Tensorflow CT Scan

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