Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerat...Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerated dehazing method based on single pixels.Unlike other methods based on regions,our method estimates the transmission map and atmospheric light for each pixel independently,so that all parameters can be evaluated in one traverse,which is a key to acceleration.Then,the transmission map is bilaterally filtered to restore the relationship between pixels.After restoration via the linear hazy model,the restored images are tuned to improve the contrast,value,and saturation,in particular to offset the intensity errors in different channels caused by the corresponding wavelengths.The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed.Comparisons with other dehazing methods and quantitative criteria(peak signal-to-noise ratio,detectable marginal rate,and information entropy difference)are introduced to verify its performance.展开更多
An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimat...An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.展开更多
Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion.To improve these limitations,this paper proposes a single image dehazing algorithm based on the V-transform a...Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion.To improve these limitations,this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior,in which a hazy RGB image is converted into the HSI color space,and each component H,I and S is processed separately.The hue component H remains unchanged,the saturation component S is stretched after being denoised by a median filter.In the procession of intensity component,a quad-tree algorithm is presented to estimate the atmospheric light,the dark channel prior and the V-transform are used to estimate the transmission map.To reduce the computational complexity,the intensity component I is decomposed by the V-transformfirst,coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image,and the guided filter is finally employed to refine the coarse transmission map.For images with sky regions,the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map.The proposed algorithm has low time complexity and performs well on a wide variety of images.The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.展开更多
Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo...Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area,because the sky region does not meet the prior assumption.For this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I.Based on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided filter.The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise,and stretched by multiplying a constant related to the initial transmission map.Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.展开更多
针对有雾天气下无人机航拍视觉系统的能见度低,航拍图像对比度和色彩保真度差等问题,基于暗原色先验规律以及雾图的物理模型提出了一种雾天降质图像去雾处理技术。从图像复原和增强两个角度出发,分别建立了户外图像全局去雾和对比度自...针对有雾天气下无人机航拍视觉系统的能见度低,航拍图像对比度和色彩保真度差等问题,基于暗原色先验规律以及雾图的物理模型提出了一种雾天降质图像去雾处理技术。从图像复原和增强两个角度出发,分别建立了户外图像全局去雾和对比度自适应调整的最优化模型,从而能够直接复原得到高质量的去除雾干扰的图像并且估算出雾的浓度。对一系列户外带雾图像的分组实验表明,该方法可以快速有效地提高带雾图像的对比度和色彩清晰度,获得满意的视觉效果。另外,该方法克服了Kai ming He方法处理时间过长的缺陷,平均处理时间仅为原方法的10%左右,显著缩短了运算时间,为在工程项目中实现图像的实时去雾处理提供了理论依据。展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.U1664264 and U1509203)
文摘Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerated dehazing method based on single pixels.Unlike other methods based on regions,our method estimates the transmission map and atmospheric light for each pixel independently,so that all parameters can be evaluated in one traverse,which is a key to acceleration.Then,the transmission map is bilaterally filtered to restore the relationship between pixels.After restoration via the linear hazy model,the restored images are tuned to improve the contrast,value,and saturation,in particular to offset the intensity errors in different channels caused by the corresponding wavelengths.The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed.Comparisons with other dehazing methods and quantitative criteria(peak signal-to-noise ratio,detectable marginal rate,and information entropy difference)are introduced to verify its performance.
基金supported by the National Natural Science Foundation of China(61075013)the Joint Funds of the Civil Aviation(61139003)
文摘An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.
基金Supported by National Natural Science Foundation of China(61571046).
文摘Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion.To improve these limitations,this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior,in which a hazy RGB image is converted into the HSI color space,and each component H,I and S is processed separately.The hue component H remains unchanged,the saturation component S is stretched after being denoised by a median filter.In the procession of intensity component,a quad-tree algorithm is presented to estimate the atmospheric light,the dark channel prior and the V-transform are used to estimate the transmission map.To reduce the computational complexity,the intensity component I is decomposed by the V-transformfirst,coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image,and the guided filter is finally employed to refine the coarse transmission map.For images with sky regions,the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map.The proposed algorithm has low time complexity and performs well on a wide variety of images.The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.
基金Supported by the National Natural Science Foundation of China(61571046)the National Key Research and Development Program of China(2017YFF0209806)
文摘Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area,because the sky region does not meet the prior assumption.For this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I.Based on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided filter.The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise,and stretched by multiplying a constant related to the initial transmission map.Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.
文摘针对有雾天气下无人机航拍视觉系统的能见度低,航拍图像对比度和色彩保真度差等问题,基于暗原色先验规律以及雾图的物理模型提出了一种雾天降质图像去雾处理技术。从图像复原和增强两个角度出发,分别建立了户外图像全局去雾和对比度自适应调整的最优化模型,从而能够直接复原得到高质量的去除雾干扰的图像并且估算出雾的浓度。对一系列户外带雾图像的分组实验表明,该方法可以快速有效地提高带雾图像的对比度和色彩清晰度,获得满意的视觉效果。另外,该方法克服了Kai ming He方法处理时间过长的缺陷,平均处理时间仅为原方法的10%左右,显著缩短了运算时间,为在工程项目中实现图像的实时去雾处理提供了理论依据。
