深度残差学习下的光源颜色估计  

Illuminant estimation via deep residual learning

作　　者：崔帅[1] 张骏[1] 高隽[1] Cui Shuai;Zhang Jun;Gao Jun(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学计算机与信息学院

出　　处：《中国图象图形学报》2019年第12期2111-2125,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61876057,61403116)~~

摘　　要：目的颜色恒常性通常指人类在任意光源条件下正确感知物体颜色的自适应能力,是实现识别、分割、3维视觉等高层任务的重要前提。对图像进行光源颜色估计是实现颜色恒常性计算的主要途径之一,现有光源颜色估计方法往往因局部场景的歧义颜色导致估计误差较大。为此,提出一种基于深度残差学习的光源颜色估计方法。方法将输入图像均匀分块,根据局部图像块的光源颜色估计整幅图像的全局光源颜色。算法包括光源颜色估计和图像块选择两个残差网络:光源颜色估计网络通过较深的网络层次和残差结构提高光源颜色估计的准确性;图像块选择网络按照光源颜色估计误差对图像块进行分类,根据分类结果去除图像中误差较大的图像块,进一步提高全局光源颜色估计精度。此外,对输入图像进行对数色度预处理,可以降低图像亮度对光源颜色估计的影响,提高计算效率。结果在NUS-8和重处理的Color Checker数据集上的实验结果表明,本文方法的估计精度和稳健性较好;此外,在相同条件下,对数色度图像比原始图像的估计误差低10%15%,图像块选择网络能够进一步使光源颜色估计网络的误差降低约5%。结论在两组单光源数据集上的实验表明,本文方法的总体设计合理有效,算法精度和稳健性好,可应用于需要进行色彩校正的图像处理和计算机视觉等领域。Objective Color constancy refers to the human ability that allows the brain to recognize an object as having a consistent color under varying illuminants. Color constancy has become an important prerequisite of high-level tasks, such as recognition, segmentation, and 3 D vision. In the computer vision community, the goal of computational color constancy is to remove illuminant color casts and obtain accurate color representations for images. Therefore, illuminant estimation is an important means to achieve computational color constancy, which is a difficult and underdetermined problem because the observed image color is influenced by unknown factors, such as scene illuminants and object reflections. Illuminant estimation methods can be categorized into two classes: statistics-based(or static) and learning-based methods. Statistics-based methods estimate the illuminant based on the statistical properties(e.g., reflectance distributions) of the image. Learning-based methods learn a model from training images then estimate the illuminant using the model. Convolutional neural networks(CNNs) are very powerful methods of estimating illuminants, and many competitive results have been obtained with CNN-based methods. We propose a CNN-based illuminant estimation algorithm in this study. We use deep residual learning to improve network accuracy and a patch-selecting network to overcome the color ambiguity issue of local patches. Method We uniformly sample local patches from the image, estimate the local illuminant of each patch individually, and generate a global illuminant estimation of the entire image by combining the local illuminants. We use a 64×64 patch size in the patch sampling to guarantee the estimation accuracy of the local illuminant and provide sufficient training inputs without data augmentation. The proposed approach includes two residual networks, namely, illuminant estimation net(IEN) and patch selection net(PSN). IEN estimates the local illuminant of image patches. To improve the estimation accuracy of

关 键 词：视觉光学 颜色恒常性 光源颜色估计 深度残差学习 对数色度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]