Single image dehazing using a new color channel

Images with hazy scene suffer from low-contrast, which reduces the visible quality of the scene, thus making object detection a more challenging task. Low-contrast can result from foggy weather conditions during image acquisition. Dehazing is a process of removal of haze from the photography of a hazy scene. Single-image dehazing based on dark channel priors are well-known techniques in this field. However, the performance of such techniques is limited to priors or constraints. Moreover, this type of method fails when images have sky-region. So, a method is proposed, which can restore the visibility of hazy images. First, a hazy image is divided into blocks of size 32 × 32, then the score of each block is calculated to select a block having the highest score. Atmospheric light is calculated from the selected block. A new color channel is considered to remove atmospheric scattering, obtained channel value and atmospheric light are then used to calculate the transmission map in the second step. Third, radiance is computed using a transmission map and atmospheric light. The illumination scaling factor is adopted to enhance the quality of a dehazed image in the final step. Experiments are performed on six datasets namely, I-HAZE, O-HAZE, BSDS500, FRIDA, RESIDE dataset and natural images from Google. The proposed method is compared against 11 state-of-the-art methods. The performance is analyzed using fourteen quantitative evaluation metrics. All the results demonstrate that the proposed method outperforms 11 state-of-the-art methods in most of the cases.     

基于混合远景和近景区域暗通道算法的图像去雾研究

为了提高图像去雾效果,提出混合远景和近景区域暗通道算法。首先建立远景和近景区域暗通道,为近景区域最小值通道和远景区域最小值通道之和,不同区域设置不同的调节系数;接着估计大气光值,通过混合暗通道远景区域的前50个像素点颜色值的平均值获得,增设校正系数;然后导向滤波,采用自适应滑动窗口优化透射率;最后给出了算法流程。实验仿真显示所提算法去雾图像视觉效果较好,定量分析指标对比度、可见边对比度值较优。     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

基于三角形循环二分算法的图像去雾研究

为了提高图像去雾效果,采用三角形循环二分算法处理图像。首先分析基本暗通道算法图像去雾;接着基于三角形循环二分算法对大气光值精确计算,将图像沿对角线分成4个三角形区域,只对雾浓度最较高的区域进行二分图形划分,选取三角形最长边的中点,连接到对应三角形最长边的顶点,循环执行直到满足预先设定的面积阈值;然后自适应调节透射率,避免色彩失真;最后给出了算法流程。实验仿真显示,本文算法去雾后的图像在视觉上更加清晰,可见边集合数目比、平均梯度比值、直方图相似度系数、综合评价指标相比其他算法较好,能够满足图像去雾对清晰度、信息量的要求。     

基于视觉颜色感知——光学相似的图像去雾方法

针对雾霾环境影响致使户外获取的图像质量严重下降问题，提出了一种基于视觉颜色感知—光学相似的图像去雾方法。充分利用人眼感知颜色的视觉机理，结合图像的相似性原理，构造了光学相似度函数，建立了新的基于视觉颜色感知—光学相似的图像去雾模型并设计相关算法，进而进行仿真验证。仿真实验结果表明，提出的方法在对有雾图像清晰化处理过程中效果明显，并与现有的图像去雾方法在主观视觉和客观量化方面进行图像去雾效果对比，进一步表明提出的方法在清晰化含雾图像处理中取得了较好的效果。     

结合高斯混合模型的改进暗通道图像去雾方法

针对经典的暗通道先验方法在处理含有大面积天空的有雾图像时,去雾图像的天空区域出现不同程度的颜色失真等问题,提出了一种结合高斯混合模型的改进暗通道图像去雾方法。首先,采用高斯混合模型对有雾图像进行建模,然后用期望最大化(Expectation maximization,EM)算法优化模型参数,从而将有雾图像分割成天空区域和非天空区域。其次,根据天空区域暗通道值的不同将其分为淡雾区、中雾区和浓雾区,分别估计透射率。并结合大气散射模型得到复原图像。最后,采用高动态范围图像自适应局部色调映射方法提升复原图像的亮度。实验结果表明,该方法有效地解决了经典暗通道先验方法去雾时产生的天空失真问题,且复原后的图像更清晰、视觉效果更好。     

基于色彩空间单一图像像素级去雾算法

基于雾会降低图像对比度以及边界模糊的实际情况,提出一种基于颜色空间的单幅图像去雾算法。首先计算每个像素在RGB颜色空间中距离灰阶线(原点与点(255,255,255)所确定的直线)的距离,确定深度图像;然后根据灰阶线距离计算每个像素在颜色空间中的新位置,进而获得去雾后的图像。算法具有良好的实用性和并行计算可行性。实验结果表明:算法显著增强了图像的对比度、颜色饱和度等,具有良好的去雾效果。     

一种边缘优化的暗通道去雾算法

针对暗通道先验去雾算法大部分的时间都消耗在对透射率的优化上的问题, 对暗通道去雾算法进行了改进, 提出了边缘优化的暗通道去雾算法。使用边缘算子从粗略估计的透射率中提取边缘, 对边缘及周围扩展区域内的像素采用差值抠图法优化图像中场景深度变化明显的区域的透射率。实验结果证明, 边缘优化的暗通道去雾算法在得到与原始算法基本一致的去雾结果的同时, 平均计算时间仅为原算法时间的60%左右, 有效减少了计算量, 提高了去雾算法的运算速度。     

改进AOD-Net的道路交通图像去雾算法

针对现有图像去雾算法在处理道路交通图像时无法同时兼顾去雾效果和实时性的问题,本文以快速一体化网络去雾算法(AOD-Net)为基础进行改进.首先,在AOD-Net中添加SE通道注意力,以自适应的方式分配通道权重,关注重要特征;其次,引入金字塔池化模块,扩大网络的感受野,并融合不同尺度特征,更好地捕捉图像信息;最后,使用复合损失函数同时关注图像像素信息和结构纹理信息.实验结果表明,改进后的AOD-Net算法对道路交通图像去雾后的峰值信噪比提升了2.52 dB,结构相似度达到了91.2%,算法复杂度和去雾耗时略微增加,但仍满足实时要求.     

结合大气散射模型的生成对抗网络去雾算法

为了提高图像去雾的性能, 提出结合大气散射模型生成对抗网络的去雾算法. 算法在pix2pix GAN基础上进行改进, 将网络的生成器改进成双解码器结构,通过双解码器分别生成无雾图像和透射率图, 并结合大气散射模型还原雾图像,以进一步提高图像分解的质量. 在马尔科夫判别器结构中,采用反向学习机制代替随机裁剪机制,以有效降低因采用随机裁剪算法而导致的判断结果不准确的概率. 在原有的损失函数上,加入雾霾损失函数,提高图像转化的质量. 在STOS和NYU数据集上进行消融实验和对比实验. 大量实验表明所提出方法在PSNR和SSIM指标上比原算法Pix2pix GAN有所提高, 且均优于现有去雾算法,复原图像具有清晰度高、噪声低、纹理丰富的优点.