基于CNN的单幅雾天图像视觉增强

Pooja Pandey, Rashmi Gupta, Nidhi Goel
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引用次数: 0

摘要

从单个雾场景中去除雾是一项乏味的工作。现有的一些方法是基于各种约束和假设来评估无雾图像或去雾图像。近年来,研究人员将深度神经网络算法应用于去雾图像的计算。受近年来研究工作的启发,本文旨在估计基于卷积神经网络(CNN)的无雾图像。利用CNN的深度架构学习不同的霾相关特征。利用这些提取的特征,估计出最终的去雾图像。并将结果与现有的分析方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision Enhancement of Single Foggy Image using CNN
Fog removal from a single foggy scene is a tedious piece of work. Some of the existing methodologies are based on various constraints and assumptions to evaluate fog free image or defog image. Recent researchers have applied deep neural network algorithms to calculate defog image. Motivated by the recent works, this paper aims to estimate fog free image entrenched on Convolutional Neural Network (CNN). Different haze relevant features are learned using deep architecture of CNN. Using these extracted features, final defog image has been estimated. Results are compared with the existing methodologies for analysis purpose as well.
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