一种基于拉普拉斯方程的图像彩色化方法

黑白图像的彩色化是图像处理领域一个活跃的、有挑战性的研究课题。在总结现有图像彩色化方法的基础上，分析了彩色化由局部向全局扩展的本质，进而提出了一种通过求解拉普拉斯方程实现颜色扩展的彩色化方法。为提高边界定位能力，设计了加权形式的差分方案。与同是采用偏微分方程的Saprio彩色化方法相比，该方法与其控制颜色扩展的机制不同，图像彩色化效果要明显优于Saprio方法。     

基于多颜色融合及投影特征的跟踪算法

提出一种基于多颜色空间信息融合和投影向量的目标跟踪算法,用以解决传统camshift不能描述目标的空间信息等缺陷。该算法利用Y、U、V、H等多种颜色分量的统计直方图进行信息融合来描述目标颜色特征,通过水平和垂直投影直方图描述目标的空间位置信息。该算法可以反映跟踪目标的部分空间信息,并具备一定的形变适应性。实验表明,该算法能够较好地改进运动目标的跟踪效果。     

类肤色背景下的人脸追踪改进算法

针对人脸追踪过程中,基于目标色彩特征的CamShift(continuously adaptive mean-shift)算法受类肤色背景干扰所导致的搜索框偏移及尺寸异常问题,提出了一种结合肤色分割及追踪监测机制的人脸追踪改进算法。在YCbCr色彩空间的Cb、Cr分量内采用非参数肤色分割模型及SVM(support vector machines)构建特定于当前视频序列的联合肤色分割模型,以由粗至细的方式去除视频帧中类肤色背景。随后,在Cr分量内构建CamShift算法色彩直方图并进行人脸追踪。考虑在追踪过程中,当场景或光照强度改变时易出现的联合肤色分割模型及CamShift算法色彩直方图失效问题,采用拉依达准则（pauta criterion）判断追踪窗口内Cr分量均值的异常,当监测到异常值时即判定当前视频帧人脸追踪失败,使用Adaboost(adaptive boosting)算法构建的人脸检测器进行人脸复检并重构CamShift算法色彩直方图及联合肤色分割模型。在OTB-2015目标追踪数据集中进行测试,实验结果表明,所提算法在类肤色背景下相比原始CamShift算法对人脸目标的...     

一种基于空间直方图的Mean-Shift跟踪算法*

经典的Mean-Shift跟踪算法以颜色直方图为特征对目标进行跟踪。颜色直方图反映的是图像中颜色的组成情况,即出现了哪些颜色以及各种颜色出现的次数。颜色直方图具有旋转不变性、缩放不变性等优点,经常用于图像检索,即通过比较颜色直方图的差异来衡量两幅图像在颜色全局分布上的差异。但是颜色直方图不能反映颜色的空间分布特征,当跟踪目标与背景色颜色相近时可能造成错误跟踪,导致跟踪失败。考虑目标颜色空间分布特征,将空间分布信息融入颜色直方图中,提出了基于空间颜色直方图的Mean-Shift跟踪方法,全面地反映了颜色的整体分布信息和空间分布信息。在VC 6.0上利用新方法和经典Mean-Shift跟踪方法分别对发生遮挡的视频和快速运动的视频进行跟踪,实验结果表明,提出的新方法能够更好地对目标进行跟踪,避免跟踪过程中的目标丢失。     

Interactive Design and Preview of Colored Snapshots of Indoor Scenes

This paper presents an interactive system for quickly designing and previewing colored snapshots of indoor scenes. Different from high-quality 3D indoor scene rendering, which often takes several minutes to render a moderately complicated scene under a specific color theme with high-performance computing devices, our system aims at improving the effectiveness of color theme design of indoor scenes and employs an image colorization approach to efficiently obtain high-resolution snapshots with editable colors. Given several pre-rendered, multi-layer, gray images of the same indoor scene snapshot, our system is designed to colorize and merge them into a single colored snapshot. Our system also assists users in assigning colors to certain objects/components and infers more harmonious colors for the unassigned objects based on pre-collected priors to guide the colorization. The quickly generated snapshots of indoor scenes provide previews of interior design schemes with different color themes, making it easy to determine the personalized design of indoor scenes. To demonstrate the usability and effectiveness of this system, we present a series of experimental results on indoor scenes of different types, and compare our method with a state-of-the-art method for indoor scene material and color suggestion and offline/online rendering software packages.     

局部线性嵌入与模糊 C-均值聚类的红外图像彩色化算法

针对红外图像彩色化处理后清晰度低、色彩不够自然等问题,将改进后的局部线 性嵌入算法(LLE)算法引入到红外图像彩色化应用中,提出了一种 LLE 与模糊 C-均值聚类的红 外图像彩色化算法。首先通过扩大邻域范围和添加权重信息等方法改善了 LLE 算法敏感于稀疏 矩阵的缺陷,在红外和彩色模板像素矢量化空间中,利用最近邻搜索计算最佳匹配系数,经彩 色值计算将彩色模板中的颜色映射到红外图像特定区域,完成模板彩色与红外目标图像的颜色 传递。利用改进后的模糊 C-均值聚类对彩色化后的红外图像进行颜色聚类,在颜色聚类集上利 用直方图均衡化进行分段颜色均衡处理,最后将均衡化后的图像合成。将本算法与其他两种红 外彩色化算法进行仿真对比,实验结果表明,与其他两种算法相比,提出的红外图像彩色化算 法在仅仅利用目标红外图像和彩色模板下就能获得图像较为清晰、目标突出的彩色化结果。     

局部自适应的灰度图像彩色化

目的 现有的灰度图像彩色化方法为了保证彩色化结果在颜色空间上的一致性,往往采用全局优化的算法,使得图像边界区域易产生过渡平滑现象。为此提出一种局部自适应的灰度图像彩色化方法,在迁移过程中考虑局部邻域像素信息,同时自动调节邻域像素权重,在颜色正确迁移的同时保证清晰的边界信息。方法 首先结合SVM（support vector machine）和ISLIC（improved simple linear iterative clustering）算法获取彩色图像和灰度图像分类结果图;然后在分类基础上,确定灰度图像高置信度像素点,并根据图像纹理特征,在彩色图像中寻找灰度图像的像素匹配点;最后利用自适应权重均值滤波实现高置信度匹配像素点的颜色迁移,并利用迁移结果对低置信度像素点进行颜色扩散,以完成灰度图像彩色化。结果 实验结果显示,本文方法获得的彩色化迁移结果评分均高于3.5分,特别是局部放大区域评价结果均接近或高于4.0分,高于其他现有彩色化方法评价分数。表明本文方法不仅能够保证颜色迁移的准确性和颜色空间的一致性,同时也能获取颜色区分度高的边界细节信息。与现有的典型灰度图像彩色化方法相比,彩色化结果图在颜色迁移的正确性和抑制边界区域颜色的过渡平滑上都有更优的表现。结论 本文算法为灰度图像彩色化过程中抑制颜色越界问题提供了新的指导方法,能有效地应用于遥感、黑白图像/视频处理、医学图像着色等领域。     

基于背景抑制颜色分布新模型的合成式目标跟踪算法

传统的基于直方图分布的目标颜色模型, 由于跟踪过程的实时性要求其区间划分不宜过细, 因此易导致同一区间有差异的颜色难以区分; 此外, 还存在易受背景干扰的问题. 本文提出一种新的背景抑制目标颜色分布模型, 并在此基础上设计了一个合成式的目标跟踪算法. 新的颜色分布模型将一阶及二阶统计信息纳入模型, 并设计了基于人类视觉特性的权重计算方式, 能有效区分同一区间内的差异色且抑制背景颜色在模型中的比重; 算法基于该颜色模型构建目标的产生式模型, 并引入结合方向梯度直方图(Histogram of oriented gradient, HOG) 特征的相关滤波器对目标形状进行判别式建模, 同时将两个模型相互融合; 针对融合参数不易设计的难点, 分析并建立了一套定性原则, 用于判定模型各自的可信度并指导模型更新; 最终利用粒子群算法的搜索机制对候选目标的位置、尺度进行搜索, 其中适应值函数设计为两个跟踪器的融合结果. 实验结果表明, 本文算法在绝大多数情况下准确率较对比算法更优且能满足实时性要求.     

Application of the Extended Distance Transformation in digital image colorization

In this paper we present a novel colorization scheme that takes advantage of the modified morphological distance transform to propagate the color, scribbled by a user on the grayscale image. First, based on the scribbled image, the topological distance values are computed for each image pixel, describing its distance to the inserted color markers. These values are then complemented with the structural information and luminance changes derived from the original grayscale image. The distances are then used along with gradient based features to reproduce original image structures while propagating the new colors obtained during the additive color blending process. Extensive experiments performed on various kinds of natural images demonstrated the effectiveness of the proposed colorization method. They also showed that the main advantage of the presented algorithm is its computational speed and ability to produce visually pleasing colorization results promptly after providing the color information.     

基于最优聚类数和直方图匹配的图像彩色化方法

针对颜色转移彩色化算法中速度慢、效果不佳及人工干预性强等问题,提出了一种新型的彩色化算法。该算法首先解决了图像聚类中聚类数的选取问题,然后利用聚类算法分别对目标图像和源图像进行聚类分割,之后用直方图匹配算法使各目标图像块自动找到匹配的源图像块,将源图像块的颜色转移到目标图像块中,实现局部图像彩色化,最后合并各结果图像。实验结果表明,该算法比以前算法在彩色化的速度和质量上有较大改进,且自动化程度高。     

基于改进Camshift算法的钻杆计数方法

针对采用传统的Camshift算法跟踪钻杆机器时相似颜色物体间易产生干扰的问题,提出了一种基于改进Camshift算法的钻杆计数方法:使用改进的Camshift算法实时捕捉钻杆目标,并且通过设置锚点以及判断时间间隔来进行钻杆计数。改进的Camshift算法采用模版匹配的方法定位跟踪目标,智能地选择合适的方法提取颜色直方图,并引入粒子滤波器,通过一定机制与纹理特征相结合,得到跟踪目标在下一帧的准确位置。实验结果表明,改进后的算法解决了相似颜色干扰问题,并且在部分遮挡条件下仍能够准确跟踪目标,从而实现对钻杆的准确计数。     

Gradient‐Preserving Color Transfer

Color transfer is an image processing technique which can produce a new image combining one source image's contents with another image's color style. While being able to produce convincing results, however, Reinhard et al.'s pioneering work has two problems—mixing up of colors in different regions and the fidelity problem. Many local color transfer algorithms have been proposed to resolve the first problem, but the second problem was paid few attentions. In this paper, a novel color transfer algorithm is presented to resolve the fidelity problem of color transfer in terms of scene details and colors. It's well known that human visual system is more sensitive to local intensity differences than to intensity itself. We thus consider that preserving the color gradient is necessary for scene fidelity. We formulate the color transfer problem as an optimization problem and solve it in two steps—histogram matching and a gradient‐preserving optimization. Following the idea of the fidelity in terms of color and gradient, we also propose a metric for objectively evaluating the performance of example‐based color transfer algorithms. The experimental results show the validity and high fidelity of our algorithm and that it can be used to deal with local color transfer.     

A local-coloring method for night-vision colorization utilizing image analysis and fusion

A natural color mapping method has been previously proposed that matches the statistical properties (mean and standard deviation) of night-vision (NV) imagery to those of a daylight color image (manually selected as the “target” color distribution). Thus the rendered NV image appears to resemble the natural target image in terms of color appearance. However, in this prior method (termed “global-coloring”) the colored NV image may appear unnatural if the target image’s “global” color statistics are different from that of the night-vision scene (e.g., it would appear to have too much green if much more vegetation was contained in the target image). Consequently, a new “local-coloring” method is presented that functions to render the NV image segment-by-segment by taking advantage of image segmentation, pattern recognition, histogram matching and image fusion. Specifically, a false-color image (source image) is formed by assigning multi-band NV images to three RGB (red, green and blue) channels. A nonlinear diffusion filter is then applied to the false-colored image to reduce the number of colors. The final grayscale segments are obtained by using clustering and merging techniques. With a supervised nearest-neighbor paradigm, a segment can be automatically associated with a known “color scheme”. The statistic-matching procedure is merged with the histogram-matching procedure to enhance the color mapping effect. Instead of extracting the color set from a single target image, the mean, standard deviation and histogram distribution of the color planes from a set of natural scene images are used as the target color properties for each color scheme. The target color schemes are grouped by their scene contents and colors such as plants, mountain, roads, sky, water, etc. In our experiments, five pairs of night-vision images were initially analyzed, and the images that were colored (segment-by-segment) by the proposed “local-coloring” method are shown to possess much more natural and realistic coloration when compared with those produced by the previous “global-coloring” method.     

基于空间直方图的粒子滤波目标跟踪算法

空间直方图融合了目标的颜色信息和颜色的空间分布信息,比传统的颜色直方图更具有目标鉴别能力。在基于粒子滤波算法的目标跟踪系统框架中,采用简单的随机漂移模型表示系统状态模型,通过空间直方图的相似度定义来建立系统观测概率模型,提出一种基于空间直方图的粒子滤波目标跟踪算法。实验结果表明,相比传统的基于颜色直方图的粒子滤波算法,提出的算法具有更好的鲁棒性。     

有限色彩空间下的线稿上色

目的 线稿上色是由线条构成的黑白线稿草图涂上颜色变为彩色图像的过程,在卡通动画制作和艺术绘画等领域中是非常关键的步骤。全自动线稿上色方法可以减轻绘制过程中烦琐耗时的手工上色的工作量,然而自动理解线稿中的稀疏线条并选取合适的颜色仍较为困难。方法 依据现实场景中特定绘画类型常有固定用色风格偏好这一先验,本文聚焦于有限色彩空间下的线稿自动上色,通过约束色彩空间,不仅可以降低语义理解的难度,还可以避免不合理的用色。具体地,本文提出一种两阶段线稿自动上色方法。在第1阶段,设计一个灰度图生成器,对输入的稀疏线稿补充线条和细节,以生成稠密像素的灰度图像。在第2阶段,首先设计色彩推理模块,从输入的颜色先验中推理得到适合该线稿的色彩子空间,再提出一种多尺度的渐进融合颜色信息的生成网络以逐步生成高质量的彩色图像。结果 实验在3个数据集上与4种线稿自动上色方法进行对比,在上色结果的客观质量对比中,所提方法取得了更高的峰值信噪比（peak signal to noise ratio,PSNR）和结构相似性（structural similarity index measure,SSIM）值以及更低的均方误差;在上色结果的色彩指标对比中,所提方法取得了最高的色彩丰富度分数;在主观评价和用户调查中,所提方法也取得了与人的主观审美感受更一致的结果。此外,消融实验结果也表明了本文所使用的模型结构及色彩空间限制有益于上色性能的提升。结论 实验结果表明,本文提出的有限色彩空间下的线稿自动上色方法可以有效地完成多类线稿的自动上色,并且可以简单地通过调整颜色先验以获得更多样的彩色图像。     

注意力引导的交互式工笔花卉上色算法

中国传统的工笔花卉画中人工渲染上色过程繁复、技巧性较高,现有的线稿图自动上色算法难以生成自然合理的渐变色效果.文中基于条件生成对抗网络,提出注意力引导的交互式工笔花卉上色算法,自动完成花卉白描线稿到工笔花卉色图的仿真生成.首先设计刻画花朵颜色类别和布局的花色注意力图,可在训练阶段指导网络进行重要颜色特征的学习,在应用阶段作为用户与系统的交互手段,完成色彩设计.其次,在网络结构设计方面,构建并训练针对花色注意力图的局部颜色编码子网络,将注意力图的编码信息作为仿射参数,引入生成器各层的条件归一化过程中,实现生成网络全域对颜色的学习和控制.考虑到深度特征偏重刻画全局语义信息,可能损失反映线条特征的局部高频信息,在生成器网络中引入跨层连接的结构,加强线条特征的学习.实验表明,文中算法可以较好地将花卉白描线稿渲染成工笔花卉的色图,生成的图像符合真实工笔花卉画的颜色分布和特点,具有较好的艺术真实感和观赏性.     

Automatic Colorization with Improved Spatial Coherence and Boundary Localization

Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a pixel-wise prediction task and utilizing deep convolutional neural networks. Though tremendous improvements have been made, the result of automatic colorization is still far from perfect. Specifically, there still exist common pitfalls in maintaining color consistency in homogeneous regions as well as precisely distinguishing colors near region boundaries. To tackle these problems, we propose a novel fully automatic colorization pipeline which involves a boundary-guided CRF (conditional random field) and a CNN-based color transform as post-processing steps. In addition, as there usually exist multiple plausible colorization proposals for a single image, automatic evaluation for different colorization methods remains a challenging task. We further introduce two novel automatic evaluation schemes to efficiently assess colorization quality in terms of spatial coherence and localization. Comprehensive experiments demonstrate great quality improvement in results of our proposed colorization method under multiple evaluation metrics.     

基于联合直方图的运动目标检测算法

从视频序列图像中检测完整的运动目标一直是计算机视觉研究的热点,基于联合直方图提出了一种运动目标检测算法。该算法利用联合直方图来描述相邻帧间的相似性,通过对视频序列中相邻两帧图像进行逐次分块,计算相邻帧对应分块的联合直方图并结合构造的相似性指标以逐步去除背景块,算法对运动目标本身不作处理,就能有效地避免检测到的运动目标出现空洞现象,最终得到了完整的运动目标。数值实验说明了该算法具有较好的检测性。     

基于前景语义信息的图像着色算法

图像可分为前景部分与背景部分,而前景往往是视觉中心。在图像着色任务上,由于前景的类别多且情况复杂,着色困难,以至于图像中的前景部分会存在着色暗淡和细节丢失等问题。针对这些问题,提出了基于前景语义信息的图像着色算法,以改善图像着色效果,达到图像整体颜色自然、内容颜色丰富的目的。首先利用前景子网提取前景部分的低级特征和高级特征;然后将这些特征融合到全景子网训练中,以排除背景颜色信息影响并强调前景颜色信息;最后用生成损失和像素级别的颜色损失来不断优化网络,指导生成高质量图像。实验结果表明,引入前景语义信息后,所提算法在峰值信噪比（PSNR）和感知相似度（LPIPS）上有所提升,可有效改善视觉中心区域着色中的色泽暗淡、细节丢失、对比度低等问题;相比其他算法,该算法在图像整体上取得了更自然的着色效果,在内容部分上取得了显著的改进。     

主色提取的直方图峰值筛选与剔除方法

目的 针对已有主色提取方法中存在的严重误检和漏检现象以及要求主色数量固定等问题,在分析主色特征含义的基础上提出了一种用于主色提取的直方图峰值筛选与剔除算法。方法 首先根据像素的空间聚集度统计出图像的鲁棒颜色直方图,并提取其局部峰值形成候选主色集;然后根据各候选主色的隶属像素数和空间分布特征以及它们之间的共同相似像素数,对候选主色进行循环筛选;最后通过候选主色剔除过程,将隶属像素数目过少、空间分布过于分散或与其他候选主色差异较小的候选主色去掉,得到最终的图像主色。另外,针对已有主色评价方法比较片面的缺陷,设计了一个能够全面反映主色影响因素的主色综合评价模型。结果 大量的实验结果表明,本文算法提取的主色在代表图像颜色特征的有效性上超越了已有的方法,且本文算法平均评价分数是已有最高得分算法的1.1倍,相对提高了约10个百分点。结论 鉴于该算法所展示的优越性能,它在图像检索、分割和编辑等领域具有较大的潜在应用价值。