高灰度级图像的生成及多曝光融合技术研究
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摘要
灰度级层次蕴含了图像细节的重要信息，通过提高图像灰度级可以恢复出图像中丢失的许多重要信息，使图像的细节更丰富，图像更清晰。近几十年，高灰度级图像的生成和显示技术已经成为图像处理领域的研究热点之一。提高图像灰度级的技术通常包括灰度级细化和高动态范围两类。目前，对灰度级细化方法的研究较少，并且细化后图像的灰度级不丰富仍是这类方法常遇到的问题；而高动态范围图像的生成及显示效果非常依赖输入的多曝光图像。针对这些问题，主要完成了灰度级细化方法、高动态范围图像生成和多曝光融合三个方面的研究工作。
针对灰度级细化的研究主要包括：（1）提出通过给待估计的浮点数加上一个服从某种已知分布的随机变量，从这些浮点数和的四舍五入后的整数值估计出这个浮点数的方法。（2）设计和实现了一套具有补光功能的成像设备，并使用提出的算法从设计的成像设备采集的图像/视频实现灰度级细化。为了检测算法和成像设备的有效性，分别使用模拟数据和真实数据进行实验。模拟数据的实验结果表明，随着图像数量增加，恢复图像与参考图像的均方根误差不断减少，并最终收敛。真实数据的实验结果表明，恢复图像比采集图像具有更丰富的灰度级。
通过分析不同曝光图像中的强度值与辐射照度之间的关系，提出在辐射照度log值的一个小邻域内，图像的强度值与辐射照度log值之间满足线性关系。基于这个发现，设计出以高动态范围图像中强度值为变量的优化模型，该模型使每个辐射照度log值邻域内，高动态范围图像中强度值变化程度与所有曝光图像中强度值变化程度最大的尽可能保持一致。实验结果表明，提出的算法可以从少量的不同曝光图像生成高动态范围图像，而且与直接求和相比，该算法得到的图像更接近辐射照度log值。
针对多曝光融合的结果非常依赖输入图像的问题，提出两个方面改进：（1）根据背景趋势定义融合权值，使融合图像保持图像局部特征的同时，所有区域过渡自然；（2）提出去除背景趋势的图像增强方法，既压缩了不同背景环境间的亮度差异，还利用灰度级邻域内的强度值在增强前后的线性关系，对同一背景环境下的目标进行增强。为了评价算法，提出了梯度模标准差作为客观评价指标之一。实验结果表明，提出的算法得到的图像中昏暗和明亮区域内的对比度明显增强，而且图像的梯度模标准差均较小，说明该算法在保持整体视觉感受的同时，增强了局部对比度。
另外，还提出了基于模糊C均值聚类的多曝光融合方法。在该方法中，提出通过将图像中所有像素分为曝光正常和不正常两个模糊类，并根据每个像素对曝光正常类的隶属度构造出引导图像。在引导图像的作用下，使用基于窗口线性变换的全局优化算法得到融合图像。实验结果表明，从主观上来看，提出的算法得到的图像的整体视觉感受适中，而且所有区域的局部特征比较明显。从客观上来看，通过与6种不同的色调映射算法和1种多曝光融合方法进行对比，提出的算法得到的图像的熵较大，而梯度模标准差较小，说明该方法不仅突显了局部对比度，而且使图像亮度分布比较均匀。
提出的方法在高灰度级图像的生成及多曝光融合方面均有较好的表现，其中灰度级细化方法能较大程度地提高灰度级精度，而生成的高动态范围图像更接近场景辐射照度log值，实现也很简单，此外，多曝光融合方法对输入图像的依赖少，即使使用较少的不同曝光图像也能得到较好的融合图像。
Much important information about details of images is contained in grayscale levels.And the lost details can be recovered by grayscale improvement technology, whichproduces clearer images with richer details. In recent decades, high grayscale imagegeneration and visualization technique has become a focus in the area of image processing,including Grayscale Super-Resolution (GSR) and High Dynamic Range (HDR) twoaspects. So far, research on GSR is rare and images obtained by the traditional methodsstill have less grayscale levels. The results of HDR image generation and display relyheavily on differently exposed images. To address these problems, we propose severalnew methods including GSR, HDR generation and exposure fusion methods.
In the GSR method, we first propose to estimate a float number from all the integermeasurements for the sum of this float number and a random variable with a givendistribution. Then, a photographing apparatus with fine-tunable indirect fill lights isdesigned and realized, obtained from which the images/videos are used to increasegrayscale precision. To validate this approach, we test on the simulated data and thereal-world data. The experiment results show that the root mean square of errors betweenthe reconstructed image and the reference image decreases with increasing the number oflow grayscale images and finally converges. When applying into the real-world data, theresults show that the reconstructed images have richer grayscale.
Based on analyzing the relationship between the intensity value and the irradiance,we propose the linear model between the intensity values and the logarithm values ofirradiance in the small neighborhood. The optimal model is designed with the intensityvalues of HDR image as the variable, which ensures that in the neighborhood the intensitychange of HDR image be consistent with the maximum change in all Low DynamicRange (LDR) images. The results show that the proposed method can generate an HDRimage from fewer differently exposed images without estimating the camera responsefunction. Besides, compared to the summation method, images obtained by the proposedmethod are closer to the logarithm values of irradiance.
In the exposure fusion method, two improvements are proposed. First, weights are defined based on the background context to make the fused image smooth. Second, theenhancement method by removing the background context is proposed to compress thedifference between different backgrounds and simultaneously increase the local contrastbased on the linear model between the intensities before and after enhanced. To evaluatethe results objectively, we propose the Standard Derivation of Gradient Magnitude(SDGM) as an objective index. The results show that in the images obtained by theproposed method, the local contrast in the dark and bright regions is higher and SDGM issmaller, indicating that the local contrast has been enhanced and the global appearance hasbeen kept.
We also propose the exposure fusion method based on fuzzy C-means clustering, inwhich all pixels are divided into a collection of two fuzzy clusters. Based on the degree towhich each pixel belongs to the normal exposure cluster, a guided image is constructedand is combined with the Globally Optimized Linear Windowed Tone-Mapping method toobtain the fused image. The results show that the proposed method can increase the localcontrast and the global brightness and that compared to6different tone mapping methodsand1exposure fusion method, images obtained by the proposed method have biggerentropy and smaller SDGM.
Methods in this paper have good performance in high grayscale image generation andexposure fusion. The proposed GSR method can considerably increase the grayscaleprecision. The proposed HDR generation method can produce an HDR image closer to thelogarithm value of irradiance and its implementation is simple. Besides, exposure fusionmethods depend less on input images, that is, fewer exposed images can also be used toproduce good results.
针对灰度级细化的研究主要包括：（1）提出通过给待估计的浮点数加上一个服从某种已知分布的随机变量，从这些浮点数和的四舍五入后的整数值估计出这个浮点数的方法。（2）设计和实现了一套具有补光功能的成像设备，并使用提出的算法从设计的成像设备采集的图像/视频实现灰度级细化。为了检测算法和成像设备的有效性，分别使用模拟数据和真实数据进行实验。模拟数据的实验结果表明，随着图像数量增加，恢复图像与参考图像的均方根误差不断减少，并最终收敛。真实数据的实验结果表明，恢复图像比采集图像具有更丰富的灰度级。
通过分析不同曝光图像中的强度值与辐射照度之间的关系，提出在辐射照度log值的一个小邻域内，图像的强度值与辐射照度log值之间满足线性关系。基于这个发现，设计出以高动态范围图像中强度值为变量的优化模型，该模型使每个辐射照度log值邻域内，高动态范围图像中强度值变化程度与所有曝光图像中强度值变化程度最大的尽可能保持一致。实验结果表明，提出的算法可以从少量的不同曝光图像生成高动态范围图像，而且与直接求和相比，该算法得到的图像更接近辐射照度log值。
针对多曝光融合的结果非常依赖输入图像的问题，提出两个方面改进：（1）根据背景趋势定义融合权值，使融合图像保持图像局部特征的同时，所有区域过渡自然；（2）提出去除背景趋势的图像增强方法，既压缩了不同背景环境间的亮度差异，还利用灰度级邻域内的强度值在增强前后的线性关系，对同一背景环境下的目标进行增强。为了评价算法，提出了梯度模标准差作为客观评价指标之一。实验结果表明，提出的算法得到的图像中昏暗和明亮区域内的对比度明显增强，而且图像的梯度模标准差均较小，说明该算法在保持整体视觉感受的同时，增强了局部对比度。
另外，还提出了基于模糊C均值聚类的多曝光融合方法。在该方法中，提出通过将图像中所有像素分为曝光正常和不正常两个模糊类，并根据每个像素对曝光正常类的隶属度构造出引导图像。在引导图像的作用下，使用基于窗口线性变换的全局优化算法得到融合图像。实验结果表明，从主观上来看，提出的算法得到的图像的整体视觉感受适中，而且所有区域的局部特征比较明显。从客观上来看，通过与6种不同的色调映射算法和1种多曝光融合方法进行对比，提出的算法得到的图像的熵较大，而梯度模标准差较小，说明该方法不仅突显了局部对比度，而且使图像亮度分布比较均匀。
提出的方法在高灰度级图像的生成及多曝光融合方面均有较好的表现，其中灰度级细化方法能较大程度地提高灰度级精度，而生成的高动态范围图像更接近场景辐射照度log值，实现也很简单，此外，多曝光融合方法对输入图像的依赖少，即使使用较少的不同曝光图像也能得到较好的融合图像。
Much important information about details of images is contained in grayscale levels.And the lost details can be recovered by grayscale improvement technology, whichproduces clearer images with richer details. In recent decades, high grayscale imagegeneration and visualization technique has become a focus in the area of image processing,including Grayscale Super-Resolution (GSR) and High Dynamic Range (HDR) twoaspects. So far, research on GSR is rare and images obtained by the traditional methodsstill have less grayscale levels. The results of HDR image generation and display relyheavily on differently exposed images. To address these problems, we propose severalnew methods including GSR, HDR generation and exposure fusion methods.
In the GSR method, we first propose to estimate a float number from all the integermeasurements for the sum of this float number and a random variable with a givendistribution. Then, a photographing apparatus with fine-tunable indirect fill lights isdesigned and realized, obtained from which the images/videos are used to increasegrayscale precision. To validate this approach, we test on the simulated data and thereal-world data. The experiment results show that the root mean square of errors betweenthe reconstructed image and the reference image decreases with increasing the number oflow grayscale images and finally converges. When applying into the real-world data, theresults show that the reconstructed images have richer grayscale.
Based on analyzing the relationship between the intensity value and the irradiance,we propose the linear model between the intensity values and the logarithm values ofirradiance in the small neighborhood. The optimal model is designed with the intensityvalues of HDR image as the variable, which ensures that in the neighborhood the intensitychange of HDR image be consistent with the maximum change in all Low DynamicRange (LDR) images. The results show that the proposed method can generate an HDRimage from fewer differently exposed images without estimating the camera responsefunction. Besides, compared to the summation method, images obtained by the proposedmethod are closer to the logarithm values of irradiance.
In the exposure fusion method, two improvements are proposed. First, weights are defined based on the background context to make the fused image smooth. Second, theenhancement method by removing the background context is proposed to compress thedifference between different backgrounds and simultaneously increase the local contrastbased on the linear model between the intensities before and after enhanced. To evaluatethe results objectively, we propose the Standard Derivation of Gradient Magnitude(SDGM) as an objective index. The results show that in the images obtained by theproposed method, the local contrast in the dark and bright regions is higher and SDGM issmaller, indicating that the local contrast has been enhanced and the global appearance hasbeen kept.
We also propose the exposure fusion method based on fuzzy C-means clustering, inwhich all pixels are divided into a collection of two fuzzy clusters. Based on the degree towhich each pixel belongs to the normal exposure cluster, a guided image is constructedand is combined with the Globally Optimized Linear Windowed Tone-Mapping method toobtain the fused image. The results show that the proposed method can increase the localcontrast and the global brightness and that compared to6different tone mapping methodsand1exposure fusion method, images obtained by the proposed method have biggerentropy and smaller SDGM.
Methods in this paper have good performance in high grayscale image generation andexposure fusion. The proposed GSR method can considerably increase the grayscaleprecision. The proposed HDR generation method can produce an HDR image closer to thelogarithm value of irradiance and its implementation is simple. Besides, exposure fusionmethods depend less on input images, that is, fewer exposed images can also be used toproduce good results.
引文
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