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。

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。

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.

基于梯度引导偏振度估算的图像去雾

雾或霾天气下,大气粒子对光的散射作用造成光学图像细节弱化,严重影响了后续的图像分析与处理任务.现有去雾算法存在去雾后图像信息丢失、产生模糊、天空区域过增强等问题.本文从偏振视角与暗通道先验理论出发,提出了一种基于直接透射光梯度特征引导的目标偏振度估算算法,用于图像去雾.通过偏振图像获取场景与大气的偏振信息;再以暗通道先验算法估计的直接透射光强的梯度特征为引导,估算目标偏振度;将估算的目标偏振度转为大气光强,经过原理性约束与引导滤波,得到优化的大气光强,进一步求解去雾图像与优化的目标偏振度.定性实验表明:本文算法去雾后,图像具有良好的平滑度,且克服了现有去雾算法存在的可见性弱、去雾残留、天空区域过增强等问题;定量实验表明:本文算法既不会造成图像信息丢失,也不会产生过多的噪声或模糊.综合对比9种具有代表性的去雾算法,本文算法具有良好的细节恢复能力、图像熵提升能力以及色调还原能力.

基于暗通道先验的多尺度融合去雾算法

针对传统暗通道先验(DCP)算法用于图像去雾时存在对比度下降、色调偏暗和过度曝光的问题,论文提出一种基于暗通道先验的多尺度融合去雾算法。首先,该算法使用图像增强暗部细节后采用阈值分割法将类似白色背景区域进行分离;然后对大面积留白背景和其他区域分别做最亮通道取反去雾处理和导向暗通道去雾处理,将去雾处理后的两部分图像进行初始融合,有效地改善了暗通道先验在大尺度高亮区域的不适用性;最后,使用拉普拉斯金字塔的多曝光融合方法,将上述去雾处理后的两幅图像、初始融合图像以及对有雾图像做导向暗通道处理后的图像进行多尺度像素级融合,获得最终去雾效果图。仿真结果表明,该算法能够有效消除传统DCP算法在处理大范围、高亮度天空区域时产生的失真现象,改善目标边缘细节,恢复图像场景的真实色彩。

基于暗通道先验引导的图像去雾网络

基于深度学习的去雾方法多数直接学习有雾图像和无雾图像之间的映射关系,未结合有雾图像自身特点,存在雾信息检测不精确、去雾不彻底的问题。针对该问题,提出一种基于暗通道先验引导的图像去雾网络(DCPDNet)。通过卷积层提取有雾图像的浅层特征;构建2个特征增强模块(FEB)来增强图像的空间特征,该模块在2个尺度上对图像特征进行增强,即利用深层特征图实现语义特征的增强,浅层特征图对实现图像细节特征的增强;为使提取的特征更关注雾的区域,基于有雾图像中雾的成像特点设计基于引导图的特征校正模块(FCB),利用暗通道先验理论构建引导图将网络学习的注意力引导到有雾区域,对提取的深层特征图做进一步的细化和校正;利用残差结构的跳转连接,将增强的浅层特征补充网络丢失的细节特征,并经过卷积操作重建去雾后图像。实验结果证明:DCPDNet可以在保持模型轻量型及运行速度较快的情况下实现良好的去雾效果。与近年先进的去雾方法进行比较,DCPDNet不仅在效率上占有优势,其去雾效果在主观视觉感受和客观评价结果上都获得了更好的效果。

基于不均匀光照校正和优化透射率的夜间去雾

针对夜间有雾天气场景下的光照不均匀、图像不清晰问题,提出了一种基于夜间不均匀光照校正和优化透射率的去雾算法。首先针对光照不均匀问题,使用基于伽马函数的图像自适应校正算法,消除不均匀光照对图像的影响;其次,由于夜间图像大部分颜色较暗,通过改进局部大气光获得大气光值;利用Sigmoid函数融合亮通道与暗通道得到粗透射率,经过联合双边滤波细化,再对光源处透射率进行优化,并使用伽马函数增强得到最终透射率;然后根据大气散射模型得到初步恢复图像;最后经过引导滤波细化,得到最终图像。实验结果表明所提算法在夜间场景中能有效去雾,且能较好的保持图像颜色对比度。