Content-based computational chromatic adaptation

Chromatic adaptation is needed to accurately reproduce the color appearance of an image. Imaging systems have to apply a transform which converts a color of an image captured under an input illuminant to another output illuminant. This transform is called chromatic adaptation transform (CAT). Different CATs have been proposed in the literature such as von Kries, Bradford, and Sharp; both consider the adjustment of all the image spatial contents (edges, texture, and homogeneous area) in the same way. Our intuition is that CATs behave differently on the image spatial content. To verify that, we prospect to study the well-known CATs influence on the image spatial content, according to some objective criteria. Based on observations we made, new CATs are derived considering the image spatial content. To achieve that, suitable requirements for CAT are revised and rewritten in a variational formalism. Encouraging results are obtained while comparing the proposed CATs to well-known ones.     

Color constancy for visual compensation of projector displayed image

The color of a displayed image by a projector can be distorted by features of the device, the ambient light, the projection screen, and also the observer. This has raised the need to correct the image during the display to eliminate these effects and to ensure a constancy of the color appearances. In this paper, we propose models for controlling the appearance of the displayed image. We argue that depending on the target application, the computational color constancy can be specified at different steps of the formation scheme of the sensed image by a human. Based on that observation and the image formation models, we reformulate the problem of the color constancy and we show that the resulting transformations cannot be explained by von Kries theory. Two compensation algorithms are deduced. The first allows preserving the appearance of the original image, and it can be used for the constancy of the acquired image whatever the environment conditions. The second algorithm allows to simulate appearances of a sensed image in a specific conditions. It can be used for the compensation of the screen reflectance or to create special effects or the camouflage. In addition, we propose a complementary operation for the contrast compensation which is derived from the Weber’s law. Experimental results show the merits of the proposed models and algorithms.     

基于改进图像导数框架的颜色恒常性计算方法

在基于图像导数框架的颜色恒常性算法的基础上,进一步考虑边缘类型和颜色通道间的相关性对于光照色度估计的影响,提出一种基于改进图像导数框架的颜色恒常性计算方法。根据导数图像中像素的饱和度提出一种饱和度权值方案;基于光学特性对导数图像中的边缘进行分类,通过引入照度准不变量提出类型相关的边缘权值方案;将两种权值方案与图像导数框架相结合,并据此进行光照色度估计,利用von Kries对角变换对偏色图像进行校正。实验结果表明该算法有效提高了光照色度估计的准确性,改善了颜色恒常性算法的效果。     

深度残差学习下的光源颜色估计

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ColorChecker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。     

Color from black and white

Color constancy can be achieved by analyzing the chromatic aberration in an image. Chromatic aberration spatially separates light of different wavelengths and this allows the spectral power distribution of the light to be extracted. This is more information about the light than is registered by the cones of the human visual system or by a color television camera; and, using it, we show how color constancy, the separation of reflectance from illumination, can be achieved. As examples, we consider grey-level images of (a) a colored dot under unknown illumination, and (b) an edge between two differently colored regions under unknown illumination. Our first result is that in principle we can determine completely the spectral power distribution of the reflected light from the dot or, in the case of the color edge, the difference in the spectral power distributions of the light from the two regions. By employing a finite-dimensional linear model of illumination and surface reflectance, we obtain our second result, which is that the spectrum of the reflected light can be uniquely decomposed into a component due to the illuminant and another component due to the surface reflectance. This decomposition provides the complete spectral reflectance function, and hence color, of the surface as well as the spectral power distribution of the illuminant. Up to the limit of the accuracy of the finite-dimensional model, this effectively solves the color constancy problem.     

Color by correlation: a simple, unifying framework for colorconstancy

The paper considers the problem of illuminant estimation: how, given an image of a scene, recorded under an unknown light, we can recover an estimate of that light. Obtaining such an estimate is a central part of solving the color constancy problem. Thus, the work presented will have applications in fields such as color-based object recognition and digital photography. Rather than attempting to recover a single estimate of the illuminant, we instead set out to recover a measure of the likelihood that each of a set of possible illuminants was the scene illuminant. We begin by determining which image colors can occur (and how these colors are distributed) under each of a set of possible lights. We discuss how, for a given camera, we can obtain this knowledge. We then correlate this information with the colors in a particular image to obtain a measure of the likelihood that each of the possible lights was the scene illuminant. Finally, we use this likelihood information to choose a single light as an estimate of the scene illuminant. Computation is expressed and performed in a generic correlation framework which we develop. We propose a new probabilistic instantiation of this correlation framework and show that it delivers very good color constancy on both synthetic and real images. We further show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it: the gray-world and gamut-mapping algorithms are presented in this framework and we also explore the relationship of these algorithms to other probabilistic and neural network approaches to color constancy     

基于von-Kries色适应的分区颜色校正方法

通过定性分析Munsell色样集在不同光源下的von Kries校正系数随色样不同而变化的现象,本文提出了一种按照图像所属区域类型调整von Kries系数的颜色校正方法,并给出了区域判定规则及各区域von Kries系数确定方法。在GCCD测试数据集上的实验结果表明,该方法有较好的效果。     

Spectral segmentation via minimum barrier distance

Constructing a reliable affinity matrix is crucial for spectral segmentation. In this paper, we define a technique to create a reliable affinity matrix for the application to spectral segmentation. We propose an affinity model based on the minimum barrier distance (MBD). First, the image is over-segmented into superpixels; then the subset of the pixels, located in the center of these superpixels, is used to compute the MBD-based affinities of the original image, with particular care taken to avoid a strong boundary, as described in the classical model. To deal with images with faint object and random or “clutter” background, we present gradient data that are integrated with the MBD data. To capture different perceptual grouping cues, the completed affinity model includes MBD, color, and spatial cues of the image. Finally, spectral segmentation is implemented at the superpixel level to provide an image segmentation result with pixel granularity. Experiments using the Berkeley image segmentation database validate the effectiveness of the proposed method. Covering, PRI, VOI, and the F-measure are used to evaluate the results relative to several state-of-the-art algorithms.     

基于视锥细胞色适应生理机制的颜色恒常性计算

通过模拟人类底层视觉系统中的颜色恒常处理机制,提出基于视锥细胞色适应生理机制的颜色恒常性计算方法,利用视锥细胞的色适应计算代替基于灰度边缘假设的颜色恒常性计算方法所采用的Von Kries对角变换.实验结果及定量定性的分析表明本文方法比原计算模型下的算法颜色校正效果要好,能有效恢复图像中物体的本质颜色,且性能更稳定.从计算神经科学的角度,本文方法支持视锥细胞的色适应生理机制在视觉颜色恒常性中扮演重要角色的观点.     

Color management of reflective‐type LCDs in terms of adaptation of the human visual system to light‐source variation

We have investigated the color management, in terms of the color adoption property of the human visual system, of a reflective‐type TFT‐LCD (R‐LCD). Since the R‐LCD depends on ambient light as the light source, it is expected that the colorimetric color on the R‐LCD must be changed if the source of the ambient light is changed. However, due to the adaptation property of the human visual system, the eye does not perceive colorimetrically corrected colors as the same color even for an R‐LCD. In this research, first, we conducted a subjective experiment to obtain the RGB code value that is required in order to display a corresponding color on the R‐LCD under varying ambient‐light conditions. The result of the experiment shows that the corresponding color of the experimental results was reasonably approximated by the color obtained by using the von Kries model. Secondly, we proposed a color‐compensating mechanism that is described as a cascaded simple 3 × 3 linear matrix. Actual colors displayed are adjusted according to the ambient light. The evaluation of the picture quality of the R‐LCD showed that the proposed model is effective.     

Illuminant Aware Gamut‐Based Color Transfer

This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white‐balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant‐aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut‐based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant‐aware and gamut‐based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.     

基于光照无关图的阴影去除方法

为了在检测目标时排除阴影的干扰,首先论述了光照无关图的原理及其重要性质,然后在此基础上提出了一种基于光照无关图的阴影去除方法。该方法根据光学成像原理通过对图像进行变换来得到一幅与光照无关的灰度图,以达到去除阴影的目的。同时针对该方法需事先测定摄像机的光照无关角的不便之处,还提出了基于直方图统计的摄像机光照无关角判定法则。通过对大量不同场景下视频监控图像的实际测试结果表明,基于光照无关图的阴影去除方法以及基于直方图统计的光照无关角判定方法,可以有效去除目标阴影,并可准确分割目标。     

Gamut Constrained Illuminant Estimation

This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately, a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define a priori a set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms. First online version published in February, 2006     

Automatic color constancy algorithm selection and combination

In this work, we investigate how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images. We show how these properties can be used to drive, given a set of illuminant estimation algorithms, the selection of the best algorithm for a given image. The algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features. The features represent the image content in terms of low-level visual properties. The trees are trained to select the algorithm that minimizes the expected error in illuminant estimation. We also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms’ estimations. Experimental results on the widely used Ciurea and Funt dataset demonstrate the effectiveness of our approach.     

基于颜色恒常性的低照度图像视见度增强

在彩色成像过程中,低照度是导致图像降质的一个重要因素. 本文提出了一种新的基于颜色恒常性的低照度图像视见度增强算法. 为了避免场景光源的影响,提出了像素有效集的概念. 基于灰色调算法的灰度像素假设,利用有效像素估计光 照的颜色;在后处理阶段,利用有效像素的灰度级范围确定直方图剪裁的上下限. 实验表明,算法有效地校正了图像 的颜色、对比度和亮度,从而增强了图像的视见度,且不会产生Retinex 算法所固有的灰化效应和Halo 效应.     

Spectralization: Reconstructing spectra from sparse data

Traditional RGB reflectance and light data suffers from the problem of metamerism and is not suitable for rendering purposes where exact color reproduction under many different lighting conditions is needed. Nowadays many setups for cheap and fast acquisition of RGB or similar trichromatic datasets are available. In contrast to this, multi‐ or even hyper‐spectral measurements require costly hardware and have severe limitations in many cases. In this paper, we present an approach to combine efficiently captured RGB data with spectral data that can be captured with small additional effort for example by scanning a single line of an image using a spectral line‐scanner. Our algorithm can infer spectral reflectances and illumination from such sparse spectral and dense RGB data. Unlike other approaches, our method reaches acceptable perceptual errors with only three channels for the dense data and thus enables further use of highly efficient RGB capture systems. This way, we are able to provide an easier and cheaper way to capture spectral textures, BRDFs and environment maps for the use in spectral rendering systems.     

基于独立成分分析和IHS变换域的灰度可见光和红外图像融合

针对灰度可见光和红外图像的融合图像缺乏色彩信息、图像的高阶信息在变换域中统计独立性不足的缺陷, 提出一种基于独立分量分析和IHS (亮度-色度-饱和度) 变换域的融合方法. 该方法利用IHS 变换域能够有效分离图像亮度分量和彩色信息的优势, 对灰度可见光图像建立灰度图像的彩色传递模型. 利用各分量的独立性进行基于独立分量分析和IHS 变换域的图像融合, 并得到最终的彩色融合图像, 使融合图像更加符合人类视觉要求. 仿真实验验证了所提出算法的有效性.

     

监督颜色校正方法研究

消除由于光照等条件变化而对图像颜色值产生的影响是进行彩色图像的识别和分析的关键。该文从表面反射的有限维线性模型出发，在理想情况下导出了颜色值从非标准光照到标准光照的线性转换关系。实际上，由于成像系统本身采用了一些处理技术，使得转换关系应为非线性。文中提出了新的监督颜色校正方法，通过在图像摄取环境中放置监督色板，井借鉴监督学习的思想，分别采用线性回归、多项式回归和BP神经网络三种方法来求解转换关系，从而对颜色值进行校正。试验结果表明，三种方法都达到了较好的校正效果，且应用条件几乎不受限制。相比之下，多项式方法优于其它两种方法。