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
Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog ...Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.展开更多
In underwater scenes,the quality of the video and image acquired by the underwater imaging system suffers from severe degradation,influencing target detection and recognition.Thus,restoring real scenes from blurred vi...In underwater scenes,the quality of the video and image acquired by the underwater imaging system suffers from severe degradation,influencing target detection and recognition.Thus,restoring real scenes from blurred videos and images is of great significance.Owing to the light absorption and scattering by suspended particles,the images acquired often have poor visibility,including color shift,low contrast,noise,and blurring issues.This paper aims to classify and compare some of the significant technologies in underwater image defogging,presenting a comprehensive picture of the current research landscape for researchers.First we analyze the reasons for degradation of underwater images and the underwater optical imaging model.Then we classify the underwater image defogging technologies into three categories,including image restoration approaches,image enhancement approaches,and deep learning approaches.Afterward,we present the objective evaluation metrics and analyze the state-of-the-art approaches.Finally,we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.展开更多
Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast ...Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast limited adaptive histogram equalization （CLAHE） algorithm, a curvelet transform and contrast adaptive clip histogram equalization （HE）-based foggy day image enhancement algorithm is proposed. The proposed algorithm transforms an image to the curvelet domain and enhances the image detail information via a nonlinear transformation of high frequency curvelet coefficients. After curvelet reconstruction, the contrast adaptive clip HE method is adopted to enhance the total image contrast and the foggy day image contrast and detail information. During the histogram clipping process, the clip limit value is adaptively selected based on image contrast and the sub-block image histogram variance. A comparative analysis of the foggy day image enhancement results are obtained by applying CLAHE, and some classical single image defogging algorithms and the proposed algorithm are also conducted to prove the effectiveness of the proposed algorithm with objective parameters.展开更多
Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image detail...Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.展开更多
To reduce the flicker artifacts caused by video defogging,a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed.First,an inter frame consistency constraint ...To reduce the flicker artifacts caused by video defogging,a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed.First,an inter frame consistency constraint is constructed and applied to background modeling.Second,the extracted background is defogged with an improved static defogging approach.Third,the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and background.Experimental results show that our algorithm can remove fog effectively and preserve the temporal coherence well.展开更多
针对目前去雾方法对于明亮区域的处理效果及抗噪性能较差的问题,提出基于透射率多重引导与锐化补偿的图像去雾算法(Image Defogging Algorithm Based on Transmittance Multiple Guidance and Sharpening Compensation,DTGSC)。该算法...针对目前去雾方法对于明亮区域的处理效果及抗噪性能较差的问题,提出基于透射率多重引导与锐化补偿的图像去雾算法(Image Defogging Algorithm Based on Transmittance Multiple Guidance and Sharpening Compensation,DTGSC)。该算法首先运用阈值分割的方法求解大气光值,通过对原图中白色区域进行定义分割,提升了大气光值的取值精度;其次为保证所提模型能够有效处理图像中的不同区域,设计了多重引导的方法进行透射率取值,将明亮区域的失真问题转换为缩减透射率取值误差问题。此外,将高斯滤波引入到图像三通道中进行降噪处理,在实现去雾的同时并提升模型的抗噪性能;最后使用图像锐化的方法对去雾结果进行增强,并通过设定目标调整亮度,完成当前亮度向目标亮度的深度补偿,实现去雾后图像边缘细节与可视化效果的联合优化。实验结果表明,所提算法在四种数据集下得到的图像MSE平均值为11.07,PSNR平均值为39.78 dB,SSIM平均值为87.83%,在薄雾数据集上的平均去雾时间达到0.63 s。相对于DCMPNet算法而言,MSE值平均缩减20.54 dB,PSNR值平均提升5.57 dB,SSIM值平均提升2.52%,去雾效率平均提升0.08s。以上实验结果验证了所提算法的有效性与优越性。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.
基金supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2022MF249)。
文摘Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.
基金Project supported by the National Natural Science Foundation of China(No.61702074)the Liaoning Provincial Natural Science Foundation of China(No.20170520196)the Fundamental Research Funds for the Central Universities,China(Nos.3132019205 and 3132019354)。
文摘In underwater scenes,the quality of the video and image acquired by the underwater imaging system suffers from severe degradation,influencing target detection and recognition.Thus,restoring real scenes from blurred videos and images is of great significance.Owing to the light absorption and scattering by suspended particles,the images acquired often have poor visibility,including color shift,low contrast,noise,and blurring issues.This paper aims to classify and compare some of the significant technologies in underwater image defogging,presenting a comprehensive picture of the current research landscape for researchers.First we analyze the reasons for degradation of underwater images and the underwater optical imaging model.Then we classify the underwater image defogging technologies into three categories,including image restoration approaches,image enhancement approaches,and deep learning approaches.Afterward,we present the objective evaluation metrics and analyze the state-of-the-art approaches.Finally,we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.
基金supported by the National Natural Science Foundation of China(61631009,41704103)
文摘Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast limited adaptive histogram equalization （CLAHE） algorithm, a curvelet transform and contrast adaptive clip histogram equalization （HE）-based foggy day image enhancement algorithm is proposed. The proposed algorithm transforms an image to the curvelet domain and enhances the image detail information via a nonlinear transformation of high frequency curvelet coefficients. After curvelet reconstruction, the contrast adaptive clip HE method is adopted to enhance the total image contrast and the foggy day image contrast and detail information. During the histogram clipping process, the clip limit value is adaptively selected based on image contrast and the sub-block image histogram variance. A comparative analysis of the foggy day image enhancement results are obtained by applying CLAHE, and some classical single image defogging algorithms and the proposed algorithm are also conducted to prove the effectiveness of the proposed algorithm with objective parameters.
文摘Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.
基金Supported by the National Natural Science Foundation of China(61571046)the 2020 Postgraduate Curriculum Construction Project of Beijing Forestry University(HXKC2005)
文摘To reduce the flicker artifacts caused by video defogging,a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed.First,an inter frame consistency constraint is constructed and applied to background modeling.Second,the extracted background is defogged with an improved static defogging approach.Third,the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and background.Experimental results show that our algorithm can remove fog effectively and preserve the temporal coherence well.
文摘针对目前去雾方法对于明亮区域的处理效果及抗噪性能较差的问题,提出基于透射率多重引导与锐化补偿的图像去雾算法(Image Defogging Algorithm Based on Transmittance Multiple Guidance and Sharpening Compensation,DTGSC)。该算法首先运用阈值分割的方法求解大气光值,通过对原图中白色区域进行定义分割,提升了大气光值的取值精度;其次为保证所提模型能够有效处理图像中的不同区域,设计了多重引导的方法进行透射率取值,将明亮区域的失真问题转换为缩减透射率取值误差问题。此外,将高斯滤波引入到图像三通道中进行降噪处理,在实现去雾的同时并提升模型的抗噪性能;最后使用图像锐化的方法对去雾结果进行增强,并通过设定目标调整亮度,完成当前亮度向目标亮度的深度补偿,实现去雾后图像边缘细节与可视化效果的联合优化。实验结果表明,所提算法在四种数据集下得到的图像MSE平均值为11.07,PSNR平均值为39.78 dB,SSIM平均值为87.83%,在薄雾数据集上的平均去雾时间达到0.63 s。相对于DCMPNet算法而言,MSE值平均缩减20.54 dB,PSNR值平均提升5.57 dB,SSIM值平均提升2.52%,去雾效率平均提升0.08s。以上实验结果验证了所提算法的有效性与优越性。
