针对车载相机受雪花、雾霾影响,导致采集图像出现雪花遮挡和雾霾面纱效应问题,基于图像边缘纹理和图像色彩分离重建的思想,提出功能解耦、双重监督的雪霾消除网络。所提算法通过对图像边缘纹理和色彩信息进行分离重建,将雪霾消除任务解...针对车载相机受雪花、雾霾影响,导致采集图像出现雪花遮挡和雾霾面纱效应问题,基于图像边缘纹理和图像色彩分离重建的思想,提出功能解耦、双重监督的雪霾消除网络。所提算法通过对图像边缘纹理和色彩信息进行分离重建,将雪霾消除任务解耦为背景纹理修复与色彩重建两个子任务,并用双生成对抗网络分别进行边缘纹理和色彩特征的协同重建。算法在SRRS-6000数据集上进行消融测试,验证了双重监督对网络加速收敛的有效性和噪声消除的显著效果,模型在Snow100K-S、Snow100K-M、Snow100K-L、I&O-Haze数据集上进行测试,峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)分别达到33.29 dB和0.94、32.8 dB和0.9316、30.13 dB和0.93、25.88 dB和0.82。实验结果表明,通过对图像去噪任务进行解耦和双重监督,取得了高效的雪花、雾霾消除效果,增强了无人驾驶辅助系统在复杂天气条件下的适应性。展开更多
目前,雾霾所引起的图像模糊问题,主流的算法主要都侧重于处理雾气,对于霾没有相关的处理.针对此缺陷,提出了一种联合K-SVD(K-singular value decomposition)稀疏算法和暗通道先验算法的全新算法,来克服雾霾引起的图像模糊问题.图像的处...目前,雾霾所引起的图像模糊问题,主流的算法主要都侧重于处理雾气,对于霾没有相关的处理.针对此缺陷,提出了一种联合K-SVD(K-singular value decomposition)稀疏算法和暗通道先验算法的全新算法,来克服雾霾引起的图像模糊问题.图像的处理主要分两个步骤:第一步是运用KSVD稀疏算法去除图像中的霾恢复出只含雾气的图像,第二步通过经典的暗通道算法去除图像上的层层雾气.计算机仿真结果表明,该方法对于图像的处理结果要优于FVR(Fast visiblity restoration)算法,暗通道先验算法和直方图均衡化算法.展开更多
The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazi...The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.展开更多
The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discer...The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.展开更多
文摘针对车载相机受雪花、雾霾影响,导致采集图像出现雪花遮挡和雾霾面纱效应问题,基于图像边缘纹理和图像色彩分离重建的思想,提出功能解耦、双重监督的雪霾消除网络。所提算法通过对图像边缘纹理和色彩信息进行分离重建,将雪霾消除任务解耦为背景纹理修复与色彩重建两个子任务,并用双生成对抗网络分别进行边缘纹理和色彩特征的协同重建。算法在SRRS-6000数据集上进行消融测试,验证了双重监督对网络加速收敛的有效性和噪声消除的显著效果,模型在Snow100K-S、Snow100K-M、Snow100K-L、I&O-Haze数据集上进行测试,峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)分别达到33.29 dB和0.94、32.8 dB和0.9316、30.13 dB和0.93、25.88 dB和0.82。实验结果表明,通过对图像去噪任务进行解耦和双重监督,取得了高效的雪花、雾霾消除效果,增强了无人驾驶辅助系统在复杂天气条件下的适应性。
文摘目前,雾霾所引起的图像模糊问题,主流的算法主要都侧重于处理雾气,对于霾没有相关的处理.针对此缺陷,提出了一种联合K-SVD(K-singular value decomposition)稀疏算法和暗通道先验算法的全新算法,来克服雾霾引起的图像模糊问题.图像的处理主要分两个步骤:第一步是运用KSVD稀疏算法去除图像中的霾恢复出只含雾气的图像,第二步通过经典的暗通道算法去除图像上的层层雾气.计算机仿真结果表明,该方法对于图像的处理结果要优于FVR(Fast visiblity restoration)算法,暗通道先验算法和直方图均衡化算法.
基金supported by the National Natural Science Foundation of China(Grant No.62072348)the National Key RD Program of China under(2019YFC1509604)the Science and Technology Major Project of Hubei Province China(Next-Generation AI Technologies)(2019AEA170)。
文摘The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2021-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.
