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  • Open Access


    CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

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

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

    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 to improve the efficiency of… More >

  • Open Access


    Dark and Bright Channel Priors for Haze Removal in Day and Night Images

    U. Hari, A. Ruhan Bevi*

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 957-967, 2022, DOI:10.32604/iasc.2022.023605

    Abstract Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the… More >

  • Open Access


    STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples

    Zhe Yu1, Bangyong Sun1,3,*, 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 >

  • Open Access


    Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

    Fayadh Alenezi*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339

    Abstract Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain… More >

  • 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


    Fast Single Image Haze Removal Method for Inhomogeneous Environment Using Variable Scattering Coefficient

    Rashmi Gupta1, Manju Khari1, Vipul Gupta1, Elena Verdú2, Xing Wu3, Enrique Herrera-Viedma4, Rubén González Crespo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1175-1192, 2020, DOI:10.32604/cmes.2020.010092

    Abstract The images capture in a bad environment usually loses its fidelity and contrast. As the light rays travel towards its destination they get scattered several times due to the tiny particles of fog and pollutants in the environment, therefore the energy gets lost due to multiple scattering till it arrives its destination, and this degrades the images. So the images taken in bad weather appear in bad quality. Therefore, single image haze removal is quite a bit tough task. Significant research has been done in the haze removal algorithm but in all the techniques, the coefficient of scattering is taken… More >

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