基于岭回归的四带图像偏色校正算法

对于在可见光范围拍摄的图像中混入近红外光引起的图像偏色问题,传统监控系统采用机械切换的红外截止滤镜来实现日夜拍摄模式的切换,但这容易产生机械故障,影响成像.本文在不改变原来CCD或CMOS传感器的基础上,采用可见光加850 nm双峰滤镜替代传统机械切换滤镜,但仍存在850 nm红外串扰问题.为解决红外干扰,本文抛弃传统的红外干扰图像偏色校正分析方法,从传感器受近红外光的影响分析入手,通过岭回归的彩色定标方法对相机自身的光谱特性进行校正.这个过程即拟合了摄像头拍摄三带图像时启用红外截止滤镜的状态.首先在实验室黑暗环境中,使用D65标准光源箱,并用850 nm的近红外光源直射已经移除红外截止滤镜的摄像头,在棚中拍摄潘通色卡的四带图像（RGB三带与IR近红外带）;随后关闭近红外光源,同机位拍摄潘通色卡的三带图像.根据岭回归算法进行彩色定标,获取三带图像与四带图像之间的校正矩阵,用于四带图像色彩校正,获得色彩自然的图像.     

优化多项式回归的四带图像偏色校正算法

鉴于三元一次多项式回归的四带图像偏色校正算法存在的局限性,为了更好地解决红外串扰的RGBIR四带图像偏色问题,从多项式回归算法的样本、数据类型及校正模型3个方面来提高四带图像的偏色校正效果;为了使得到的校正算法更佳稳健,从增加算法训练样本以及将数据转为有符号浮点型像素值来建立校正模型;根据RGB图像灰阶表达的非线性特性,将三元一次模型改为三元二次模型.实验证明,本文提出的优化方法,使得四带图像的偏色校正效果得到提高.     

彩色编码结构光测量系统器件非线性校正

在彩色编码结构光测量系统中,为解决伽马畸变和颜色串扰导致的CCD接收相位非线性问题,提出一种基于彩色灰度图像的伽马值预编码法.根据CCD对彩色三通道响应程度的差异,分别对各通道投影灰度引入不同预编码值进行预调制,同时求取串扰逆矩阵对颜色串扰进行补偿.为验证该方法的有限性,搭建光栅投影结构光三维测量系统.实验结果表明:该方法能够校正各通道光栅条纹的非正弦性,包裹相位的相位误差得到大幅减少.与已有的校正方法相比,该方法的校正效果更好.     

基于偏色校正和信息损失约束的沙尘暴降质图像增强算法

为了解决沙尘暴环境下拍摄的图像中存在的颜色偏移、对比度低等降质问题,提出基于偏色校正和信息损失约束的沙尘降质图像增强算法,主要包含偏色校正和对比度增强2个模块,在偏色校正模块中,通过分析沙尘降质图像的RGB 3个通道直方图分布特点,提出一个改进的基于高斯模型的偏色校正算法;在对比度增强模块中,通过结合基于暗通道先验和信息损失约束算法,提出一种基于大气散射模型的对比度增强算法.为了验证算法的有效性,通过与4种已有算法的大量实验结果对比发现:文中算法不但能够很好地增强不同种类的沙尘降质图像的对比度,而且可以有效地避免图像偏色现象,并且保持良好的色彩保真度和合适的亮度.     

基子AD8108的宽频带低串抗视频切换矩阵的设计

介绍了一种宽频带低串扰视频切换矩阵的设计.该矩阵采用美国ADI公司的AD8108作为宽频带视频切换矩阵的信号切换芯片,采用AT89C51作为微控制器完成矩阵切换的控制和外围界面的维护.实现的视频切换矩阵具有325MHz的带宽能力和低通道间串扰、低进出串扰的特性,同时具有-98dB的高进出隔离度,完全可以满足各行业中对高性能视频和VGA信号的切换需求.     

基于串扰影响的混合时序分析

针对产生串扰所需的耦合电容、信号翻转方向及时序信息，提出了包括串扰目标选择、串扰逻辑关系验证的混合时序分析算法。该算法在混合时序分析中引入测试生成，通过考察信号间的时序和逻辑关系来验证耦合电容处是否有串扰发生，并在串扰条件下验证电路的时序是否收敛。实验证明，该算法真实地反映了电路中串扰的分布情况，所得的延时分析结果也更为准确。     

基于直方图均衡化与MSRCR的沙尘降质图像增强算法

针对沙尘天气下图像色彩偏移严重及对比度低等问题,提出一种基于直方图均衡化与带色彩恢复的多尺度视网膜（MSRCR）增强的沙尘降质图像增强算法。通过偏色校正和图像增强两个步骤进行图像恢复,将RGB图像各通道预处理后利用限制对比度自适应直方图均衡方法得到校正后的图像,对图像采用双边滤波进行降噪处理,通过MSRCR算法进一步解决色彩失衡问题。由于处理后的图像对比度较低,存在一定色偏,利用伽马校正和基于图像分析的偏色检测及颜色校正方法进行处理得到最终结果。对大量沙尘降质图像进行仿真实验,结果表明,该算法能够有效处理不同偏色程度的沙尘图像,不仅提高了图像的对比度,而且有效避免了图像颜色偏移现象,相比GCANet、MSRCR等算法,平均时间效率提升了46.2%~94.7%。     

基于Voronoi图的自动白平衡算法

在数码相机系统中,不同色温下的图像存在偏色现象,而传统白平衡算法不能准确重现图像的真实颜色。为此,提出一种基于 Voronoi图的自动白平衡算法。利用Voronoi图统计白点数,估计当前环境的色温值,并做增益计算,根据标准值对图像颜色值进行校正。在不同背景和光源下的测试结果表明,该算法可使图像质量得到有效改善。     

基于AD8108的宽频带低串扰视频切换矩阵的设计

介绍了一种宽频带低串扰视频切换矩阵的设计。该矩阵采用美国ADI公司的AD8108作为宽频带视频切换矩阵的信号切换芯片,采用AT89C51作为微控制器完成矩阵切换的控制和外围界面的维护。实现的视频切换矩阵具有325MHz的带宽能力和低通道间串扰、低进出串扰的特性,同时具有-98dB的高进出隔离度,完全可以满足各行业中对高性能视频和VGA信号的切换需求。     

基于RGB色彩空间的四带树木图像颜色校正方法

针对图像受近红外光线参与的影响而出现偏色现象,介绍了一种基于RGB色彩空间的四带（RGB带和一个附加的近红外带）树木图像颜色校正方法. 该方法首先根据R-G差值图像,通过实验获取合适的阈值将四带树木图像二值化. 然后,利用中值滤波器和数学形态学对二值图像进行滤噪处理,提取出偏色较严重区域. 最后分区域进行颜色校正,达到消除近红外干扰的目的. 实验结果表明,该方法对部分常见含植物的场景图像有比较好的校正效果.     

Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime, which however, raises new challenges such as severe color distortion, more complex lighting conditions, and lower contrast. Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime, we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm. We first propose a human visual system (HVS) inspired color correction model, which is effective for removing the color deviation on nighttime hazy images. Then, we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion, where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids. Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast, color fidelity, and visibility. In addition, our method outperforms the state-of-the-art methods qualitatively and quantitatively.     

基于RAW格式的图像双光源白平衡

白平衡是照片处理过程中决定性的一步,它根据不同的光源消除色偏,确保原始的图像能够再现。现有白平衡算法中最为经典有效的有灰色世界法和RGB最大值法,它们都是在单光源的前提下进行白平衡处理的,可是很多情况下照片是在双光源或者混合光源的情况下拍摄的。因此,本文在研究单光源白平衡算法的基础上,直接对RAW格式的图片进行白平衡处理,提出了基于RAW格式的图像双光源白平衡算法。实验表明我们的算法能获得满意的处理效果。     

Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality.     

A methodology for extracting objective color from images

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.     

Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization

We describe color reproduction and correction of images captured by electronic cameras under multiple illumination (or lighting) conditions, relating to color device characterization for enhancing the quality of color in the obtained images. In particular, we highlight a very practical use of neuro-fuzzy modular network coactive neuro-fuzzy inference systems (CANFIS) models for this application, and discuss their strengths and weaknesses compared with other adaptive network models (e.g., multilayer perceptron (MLP)) as well as conventional lookup-table-type (TRC-matrix) methods. Our in-depth investigation based on comprehensive numerical tests with a wide variety of illumination/lighting data (180 sources of illumination) shows that the "neuro-fuzzy CANFIS with MLP local experts" possesses a remarkable generalization/approximation capacity, even under a very restricted condition where only four-illuminant data sets were permitted to be used for optimization because of efficient practical implementation subject to an industrial setting.     

Image segmentation and similarity of color-texture objects

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from textured objects under different lighting conditions.