Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One w...Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.展开更多
针对航拍图像易受雾气影响,AOD-Net(All in one dehazing network)算法对图像去雾后容易出现细节模糊、对比度过高和图像偏暗等问题,本文提出了一种基于改进AOD-Net的航拍图像去雾算法.本文主要从网络结构、损失函数、训练方式三个方面...针对航拍图像易受雾气影响,AOD-Net(All in one dehazing network)算法对图像去雾后容易出现细节模糊、对比度过高和图像偏暗等问题,本文提出了一种基于改进AOD-Net的航拍图像去雾算法.本文主要从网络结构、损失函数、训练方式三个方面对AOD-Net进行改良.首先在AOD-Net的第二个特征融合层上添加了第一层的特征图,用全逐点卷积替换了传统卷积方式,并用多尺度结构提升了网络对细节的处理能力.然后用包含有图像重构损失函数、SSIM(Structural similarity)损失函数以及TV(Total variation)损失函数的复合损失函数优化去雾图的对比度、亮度以及色彩饱和度.最后采用分段式的训练方式进一步提升了去雾图的质量.实验结果表明,经该算法去雾后的图像拥有令人满意的去雾结果,图像的饱和度和对比度相较于AOD-Net更自然.与其他对比算法相比,该算法在合成图像实验、真实航拍图像实验以及算法耗时测试的综合表现上更好,更适用于航拍图像实时去雾.展开更多
基金Projects(91220301,61175064,61273314)supported by the National Natural Science Foundation of ChinaProject(126648)supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2012170301)supported by the New Teacher Fund for School of Information Science and Engineering,Central South University,China
文摘Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.
文摘针对航拍图像易受雾气影响,AOD-Net(All in one dehazing network)算法对图像去雾后容易出现细节模糊、对比度过高和图像偏暗等问题,本文提出了一种基于改进AOD-Net的航拍图像去雾算法.本文主要从网络结构、损失函数、训练方式三个方面对AOD-Net进行改良.首先在AOD-Net的第二个特征融合层上添加了第一层的特征图,用全逐点卷积替换了传统卷积方式,并用多尺度结构提升了网络对细节的处理能力.然后用包含有图像重构损失函数、SSIM(Structural similarity)损失函数以及TV(Total variation)损失函数的复合损失函数优化去雾图的对比度、亮度以及色彩饱和度.最后采用分段式的训练方式进一步提升了去雾图的质量.实验结果表明,经该算法去雾后的图像拥有令人满意的去雾结果,图像的饱和度和对比度相较于AOD-Net更自然.与其他对比算法相比,该算法在合成图像实验、真实航拍图像实验以及算法耗时测试的综合表现上更好,更适用于航拍图像实时去雾.
