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  • Open Access


    FPD Net: Feature Pyramid DehazeNet

    Shengchun Wang1, Peiqi Chen1, Jingui Huang1,*, Tsz Ho Wong2

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

    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 terms of PSNR, SSIM, and… More >

  • Open Access


    Multiscale Image Dehazing and Restoration: An Application for Visual Surveillance

    Samia Riaz1, Muhammad Waqas Anwar2, Irfan Riaz3, Hyun-Woo Kim4, Yunyoung Nam4,*, Muhammad Attique Khan5

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

    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 of these techniques take unrealistic… More >

  • Open Access


    Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoTEnhanced Smart Cities

    Jing Zhang1,2, Xin Qi3,*, San Hlaing Myint3, Zheng Wen4

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2809-2809, 2021, DOI:10.32604/cmc.2021.17410

    Abstract This article has no abstract. More >

  • Open Access


    Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

    Jing Zhang1,2, Xin Qi3,*, San Hlaing Myint3, Zheng Wen4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2807-2824, 2021, DOI:10.32604/cmc.2021.017410

    Abstract With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities’ information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional… More >

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