一种基于Dark Channel Prior图像去雾改进算法

探测机器人视觉导航中,现场烟雾或者有色气体会直接导致回传的视频图像雾化且充满噪声,图像品质差,影响后续目标检测与跟踪。针对上述问题,本文提出了一种基于Dark Channel Prior图像去雾改进算法,该算法首先对采集到图像进行灰度化预处理,再用滤波器进行去噪处理,有效去除采图像中的噪声,使待处理图像更加平滑,最后使用Dark Channel Prior去雾算法进行去雾处理,得到高品质图像。根据仿真实验表明,本算法能够有效解决图像去雾中的噪声问题,提高传送画面的清晰度,并缩短图像处理时间,时实性更强。

Image Dehazing by Incorporating Markov Random Field with Dark Channel Prior

As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

Dark channel prior based blurred image restoration method using total variation and morphology

The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.

Single foggy image restoration based on spatial correlation analysis of dark channel prior

Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.

A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.

Dehazing algorithm using adaptive dark channel fusion and sky compensation

Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area,a dehazing algorithm using adaptive dark channel fusion and sky compensation is proposed.Firstly,according to the characteristics of minimum filtering of large window scale and small window scale in the dark channel prior,the fused dark channel is obtained by weighted fusion of the approximate depth of field relationship,thus obtaining the primary transmission.Secondly,use the down-sampling to optimize the primary transmission combined with gray scale image of haze image by fast joint bilateral filtering,then restore the original image size by up-sampling,and the compensation of the Gaussian function is used in the sky area to obtain corrected transmission.Finally,the improved atmospheric light is combined with atmospheric scattering model to recover haze-free image.Experimental results show that the algorithm can recover a large amount of detailed information of the image,obtain high visibility,and effectively eliminate the halo effect.At the same time,it has a better recovery effect on bright areas such as the sky area.

Improved dark channel image dehazing method based on Gaussian mixture model

To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.

一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法

受煤矿井下粉尘、水雾和低照度环境影响,对皮带运输系统的监测图像精准识别极为困难。针对现有去尘雾方法的图像处理结果和效率欠佳的问题,提出一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法。首先利用阈值分割结合伽马变换修正通道差,解决因低照度环境影响导致的尘雾浓度较大区域与其他区域间像素值差异不明显的问题,修正后通过引导尘雾图像做引导滤波得到更加符合实际情况的全局大气光强;然后为解决暗通道先验在尘雾浓度较大区域失效问题,引入亮通道先验进行补充,使用通道分量来辅助暗通道及亮通道透射率融合,避免因多次分割而导致的边缘像素归属问题;最后将去雾后RGB图像转至HSV空间,对亮度分量进行直方图均衡化并将均衡化前后的亮度分量进行加权融合,采用客观指标评价,选择最优聚合权值进行聚合,同时考虑去雾过程中饱和度损失和亮度分量与饱和度分量间的相关性提出饱和度自适应矫正函数,对图像饱和度进行矫正,色调分量保持不变,随后将图像转回至RGB空间,得到亮度适中、信息保留丰富和色彩鲜艳的图像;为验证所提方法的有效性,采用主观视觉、客观指标和目标检测精度及置信度进行算法对比,实验结果表明所提方法在上述4个指标上均优于被对比算法,其图像细节保留丰富,图像视觉观感更佳。

基于CLAHE-PCA的矿井低照度图像增强研究

地下矿山巷道环境往往面临光线不足,难以通过获取其暗通道图像判断岩体剥落等异常情况。针对矿井巷道暗通道图像对比度低的问题,提出了一种基于CLAHE-PCA的图像增强算法。首先使用CLAHE算法将获取的矿井巷道原始暗通道图像做对比度增强处理,然后使用自适应Gamma算法对亮度低的图像予以增加对比度矫正;将矫正后获得的灰度图转为RGB图像,通过PCA对其进行平滑处理,以便更多地还原暗通道图像的细节。以峰值信噪比、结构相似性、平均梯度和信息熵等作为评价指标,对试验结果进行验证。结果表明:该方法能够有效处理低对比度的矿井巷道图像,处理后的图像结构相似性达到93%,鲁棒性强,同时能够更多地还原图像的细节。

快速视频去雾改进算法的FPGA实现

内窥镜去雾算法在医疗领域具有广泛应用,为临床医生提供清晰、实时的图像。去雾技术虽然已经取得较大的进步,但去雾算法的复杂度较高,在内窥镜等复杂情况下硬件实现较为困难。为了在硬件上实现内窥镜实时去雾效果,对暗通道先验算法进行改进,降低硬件资源消耗和时间复杂度。该改进算法选择适合硬件的大气光照强度估计值、透射率补偿值以及采用流水线结构实现有雾图像的处理。采用Xilinx的ZYNQ7020实现该算法硬件电路,实时处理分辨率为640×480的视频图像,速度可达到260 fps,消耗LUT仅为1.28 K,寄存器619个单元。实验结果表明,相比于传统算法,改进算法具有处理速度快、功耗低、可移植性强的特点,满足内窥镜需要实时处理视频的要求。

含明亮区域的无人机遥感定位图像去雾方法

针对传统DCP去雾算法处理无人机遥感定位含雾图像时,天空或白色等明亮区域颜色易发生失真,图像整体对比度降低等问题,提出了一种自适应阈值分割的DCP去雾方法。利用灰度图像I_(gray)(x)求取图像明亮与非明亮区域的自适应阈值ThrB;根据自适应阈值ThrB将明亮区与非明亮区分割,并设计自适应修正函数M;优化由暗通道图像生成的大气耗散函数粗估计,利用双边滤波再次细化透射率,完成图像去雾复原。实验结果表明:提出方法在处理天空或反光较强的明亮区域时,能够有效避免复原后的颜色失真等问题,进一步改善遥感图像地面景物区域的处理效果,复原后整幅遥感图像的色彩饱和度和对比度明显提高,主观视觉效果有一定改善,且PSNR、FC、SSIM和CR等客观参数均有提升,有利于后续遥感定位图像分析。

基于峰值直方图均衡化的车位图像增强

车位增强算法作为自动泊车的重要组成部分,其增强结果直接影响车位线提取效果。基于此,引入了暗通道作为低频分量进行自适应对比度增强,基于多组低对比度车位图像数据,讨论了多种低对比度增强算法的适用性,针对增强算法部分区域产生的车位增强不足与过曝现象导致降低车位线提取的完整性与精度下降的问题,提出峰值直方图均衡化的快速增强算法,结合了PSNR、结构相似性、平均亮度和信息熵等作为客观评价指标,利用了霍夫直线检测统计算法增强结果的车位提取精度,并进行了验证。研究结果表明:本算法能够减少环境信息干扰,保留更多纹理细节,提升全局图像亮度与对比度,其在低照度环境下仍然具有出色的鲁棒性。本算法车位线提取精度超过90%,算法运行时间仅为37.18 ms,能够为低对比度场景下的自动泊车系统提供方法指导。

多尺度融合图像去雾方法

图像去雾能够使视觉系统适应不同的天气状况.为克服传统暗通道先验方法会在物体边界区域形成光晕效应的问题,提出一种用于估计有雾图像透射率的多尺度融合算法.应用不同大小的最小值半径得到多尺度的透射率估计值,再根据局部区域像素具有类似的透射率值这一现象,对透射率图进行多尺度融合,选择小透射图区域中最亮的像素来计算大气光值,最后使用大气散射模型恢复清晰图像.分别从视觉效果和量化指标两个方面,对比所提方法与传统的基于先验和基于深度学习的去雾方法在进行图像去雾后的效果.结果发现,针对4种典型场景,采用本研究算法去雾后的重构图像能够保留更多的结构、细节和颜色信息,避免了过分增强和边缘部分的雾残留问题,视觉效果均优于对比方法;量化指标峰值信噪比和结构相似性均高于对比方法,分别为15.65和0.78.

基于天空区域分割的快速去雾算法研究

针对传统暗通道先验去雾算法在处理户外含雾图像时,出现天空区域颜色失真和处理速度慢的问题,提出一种可自适应识别天空区域的快速去雾算法。在天空分割方面,选用图像中细节特点明显的亮度分量为研究对象,结合最大类间方差法(OTSU)和动态参数建立自适应识别天空区域算法模型,得到最佳分割阈值,分割出有雾图像的天空区域和非天空区域,并根据天空区域计算出大气光值。在提高处理速度方面,在使用引导滤波优化透射率过程中引入图像下采样算法,保证复原后图像质量的同时减少算法耗时。最后经过与多种经典算法对比,文中算法在视觉效果上细节更加自然,SSIM、PSNR和MSE的综合指标均超过其他算法,并且处理速度较快。主观和客观评价结果均表明,文中算法在视觉效果和时间效率方面都优于其他几种算法,具有一定的实用价值。

基于改进暗通道先验的海上低照度图像增强算法

针对基于暗通道先验的低照度图像增强算法在处理极端海上低光环境下图像时会存在光晕效应、色彩失真的问题,提出了1种基于暗通道先验的自适应海上低照度图像增强算法。首先,通过选取图像类型划分指标,将数据集中的图像分类,并通过Otsu方法和图像直方图分布,获取图像的区域划分阈值,将图像进行划分得到局部区域图,分析各类图像的局部区域图之间的关系;最后,通过对不同的局部区域图采用不同的改进暗通道先验算法进行处理,将1个图像中的2个增强后局部区域图合并,得到整张图像的增强结果,并对增强后图像进行主客观的图像质量评价。实验结果表明,该算法解决了现有算法在处理极端海上低照度图像时存在光晕效应和色彩失真的问题,并使不同环境下的海上低照度图像都能达到较好的恢复效果。

一种改进的暗通道先验低光照图像增强算法

针对低光照图像增强算法常见的亮度不均匀、色彩失真、图像噪点较多、细节不清晰等问题,提出了一种改进的暗通道先验低光照图像增强算法。该方法对像素值取反后的低光照图像,首先采用引导滤波,解决图像在运用最小值滤波计算暗通道时引起的块效应,其次在剔除像素为255的纯白色点干扰后进行大气光值的计算,然后引入细化系数进行透射率自适应修正使透射率更加平滑,最后采用非局部平均滤波进行噪声去除。实验表明,所提出的算法使图像的亮度增强合适,细节清晰,在Low-Light弱光图像数据集上测试图片,所得到的SSIM值比对比算法提升20.5%,PSNR值提升19.9%,无论从主观感受,还是客观评价指标等各方面,都有优化。

基于暗通道先验去雾的SDSS图像去噪研究

美国的斯隆数字巡天(sloan digital sky survey,SDSS)于2000年开始正式的巡天观测,目前已经产生了海量的光谱和图像数据,但受仪器设备本身和观测条件的影响可能会出现观测数据细节模糊或损失等问题.在进行后续数据分析和挖掘之前,对观测图像进行去噪处理是一个非常必要的步骤.基于此,将暗通道先验(dark channel prior,DCP)去雾算法应用于SDSS测光图像的去噪处理中,并与B2U方法进行对比分析.结果表明:与B2U方法相比,DCP算法去噪效果在峰值信噪比(peak signal-to-noise ratio,PSNR)上提高了7.98 dB,结构相似性(structural similari⁃ty,SSIM)上提高了7%.

用于眼底视网膜图像的去雾状杂散光算法

针对眼底视网膜图像存在雾状杂散光导致血管细节不清晰的问题,本文提出了一种基于暗通道理论并结合Gamma变换的眼底视网膜图像去雾算法,在不丢失血管细节信息的同时提高了图像的清晰度。该算法通过分别处理R、G、B通道来对图像去雾。首先,通过自适应窗口最小值滤波算法计算暗通道图像,按照亮度取前最大0.1%像素的平均值作为大气光照强度值;然后,求解图像的粗略透射率,并此基础上使用改进后的导向滤波算法对透射率进行优化;最后,通过大气散射模型和Gamma变换复原出无雾图像。实验结果表明,不同视场复原图像的信息熵、平均梯度分别平均提高18%、24%。本算法能够快速有效地去除眼底视网膜图像中存在的雾状杂散光,复原后的图像清晰自然,保留了视网膜血管细节信息。

基于自适应大气光值补偿的改进暗通道煤矿井下图像去雾算法(特邀)

视频监控是保障工业生产和人员安全的重要手段。但在一些特殊领域,监控图像常会受到粉尘散射的影响而产生雾化现象、导致图像质量退化。此外,由于这些生产环境在照明、目标景物方面具有特殊性,常用的暗通道去雾算法存在色彩偏移、过暗、光晕等现象。以煤矿井下监控图像为目标,针对井下粉尘、水雾导致图像模糊、照度低、色彩信息不丰富的问题,提出一种改进的快速暗通道算法。该算法对大气光值进行基于像素自身颜色信息的自适应补偿校正,以减小色偏的产生,并针对井下图像提出了自拟合的亮度和饱和度增强函数。除对井下图像进行处理外,还将该算法用于常用去雾图像数据集,结果表明该算法对多种场景在有效去雾的同时很好地矫正了色偏及光晕现象,并提高了亮度和色彩饱和度。该算法为井工煤矿等产业以及生活场景的去雾提供了一种新的手段。

基于雾浓度分类与暗-亮通道先验的多分支去雾网络

在图像去雾领域中,目前多数去雾模型难以维持精度与效率的平衡,高精度的模型往往伴随着复杂的网络结构,而简单的网络结构又往往会导致低质量的结果.针对该问题提出一个基于雾浓度分类与暗-亮通道先验的多分支去雾模型,通过对带雾图像分类,使用复杂度不同的网络来处理不同雾浓度的图像,可在保证精度的同时提高计算效率.模型由轻量级雾图像分类器和基于暗-亮通道先验的多分支去雾网络2部分构成:前者将带雾图像分为轻雾、中雾、浓雾3类,输出雾浓度标签;后者包含3个结构相同、宽度不同的分支网络,根据雾浓度标签选择不同的分支网络处理不同雾浓度图像,恢复至无雾图像.提出一个新的雾浓度分类方法以及基于该方法的雾浓度分类损失函数,可根据带雾图像的暗通道特征和恢复难度,结合生成图像质量和模型计算效率,得到对带雾图像合理准确的分类结果,达到去雾效果和算力需求的良好平衡.提出新的暗通道与亮通道先验损失函数,用于约束分支去雾网络,可有效提高去雾精度.实验结果表明,模型能够以更低的网络参数量和复杂度得到更优的去雾结果.