基于模糊理论和人工神经网络的图像去雾性能分析

N. Minallah, I. Ullah, M. Ashfaq, H. Mahesar
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引用次数: 0

摘要

在雾蒙蒙的环境下摄影,由于介质中存在水粒子,造成光的衰减和散射,造成严重的图像质量损失和有价值的信息损失。为了最大限度地减少雾霾的影响,提高视觉质量,本文提出了一种结合模糊理论、人工神经网络和图像融合的新技术。利用模糊推理系统对传输图进行估计。然后运用形态学运算和人工神经网络技术对存在的色散进行去除。采用反向传播、前馈、级联前馈和fitnet人工神经网络对无振荡传输图进行进一步细化。最后,使用图像融合技术恢复所有四幅图像的增强版本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Performance analysis of image Dehazing using fuzzy theory and Artificial Neural Networks
Photography in hazy environment, light attenuation and scattering caused by the water particles present in the medium, result in loss of severe image quality and loss of valuable information. In order to minimize the effect of haze and improve visual quality, this literature present a novel technique combining fuzzy theory, artificial neural networks and image fusion. Transmission map is estimated using fuzzy inference system. Then morphological operation and artificial neural network are applied to remove the halation present. Backpropagation, feedforward, cascaded-feedforward and fitnet artificial neural networks are applied on halation free transmission map for further refinement. Finally, image fusion technique is used to recover an enhanced version of all four images.
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