摘 要 针对现有 Retinex 算法中存在的色彩失真、噪声放大及光晕伪影现象等问题, 本文提出了一种基于 Retinex 理论的 改进算法. 该算法首先在 HSV 空间对亮度分量 V 通道进行增强处理, 同时在拉伸得到的对数域反射分量至一定的动态范围 时 (本文是 0 ∼ 255), 引入增强调整因子, 调整不同亮度值的增强程度来避免噪声放大及色彩失真现象; 然后在 RGB 空间, 通 过分析光晕产生的原因, 提出一种改进的高斯滤波器来消除光晕现象, 并在计算反射分量时, 通过参数调整图像颜色的保真度. 最后, 对上述两种不同颜色空间的处理结果进行加权平均作为算法的最终输出. 实验结果表明, 针对不同光照条件下的图像, 1) 该算法可以明显地改善光晕伪影现象; 2) 无色彩失真、噪声放大等问题; 3) 效果和效率优于带色彩恢复的多尺度 Retinex 算法 (Multi-scale retinex with color restoration, MSRCR) 及其他对比算法.
The seeds for this book were first planted in 2001 when Steve Seitz at the University of Wash- ington invited me to co-teach a course called “Computer Vision for Computer Graphics”. At that time, computer vision techniques were increasingly being used in computer graphics to create image-based models of real-world objects, to create visual effects, and to merge real- world imagery using computational photography techniques. Our decision to focus on the applications of computer vision to fun problems such as image stitching and photo-based 3D modeling from personal photos seemed to resonate well with our students
Chromatic aberration (also known as colour fringing) is a phenom- ena where different wavelengths of light refract through different parts of a lens system. Thus, the colour channels may not align as they reach the sensor/film. This is most notable in cheaper lenses, it is also noticeable at higher resolutions. We desire an image free of chromatic aberrations is simple, so all the planes are in focus. For a simple lens system this amounts to misaligned colour channels (red, green and blue). The misalignment is due to a uniform scaling and translation. There are many types of other chromatic aberrations. Complex lens assemblies introduce new distortions. We propose a method to deal with the simple, more common scenario. We later discuss possible ways to deal with more complex ones
his paper reports an experiment conducted to evaluate correction methods of chromatic aberrations in images acquired by a non- metric digital camera. The chromatic aberration correction methods evaluated in the experiment are classified into two kinds. One is the method to correct image coordinates by using camera calibration results of color-separated images. The other is the method based on the assumption that the magnitude of chromatic aberrations can be expressed by a function of a radial distance from the center of an image frame. The former is classified further into five types according to the difference of orientation parameters common to all colors. The latter is classified further into three types according to the order of the correction function. We adopt a linear function, a quadratic function and a cubic function of the radial distance as a correction function. We utilize a set of 16 convergent images shooting a white sheet with 10 by 10 black filled circles to carry out camera calibration and estimate unknown coefficients in the correction function by means of least squares adjustment. We evaluate the chromatic aberration correction methods by using a normal image shooting a white sheet with 14 by 10 black filled circles. From the experiment results, we conclude that the method based on the assumption that the magnitude of chromatic aberrations can be expressed by a cubic function of the radial distance is the best method of the evaluated methods, and would be able to correct chromatic aberrations satisfactorily enough in many cases
OpenCV在计算机视觉领域扮演着重要的角色。作为一个基于开源发行的跨平台计算机视觉库,OpenCV 实现了图像处理和计算机视觉方面的很多通用算法。本书以当前最新版本的 OpenCV 最常用最核心的组件模块为索引,深入浅出地介绍了OpenCV2和 OpenCV3中的强大功能、性能,以及新特性。书本配套的 OpenCV2和 OpenCV3双版本的示例代码包中,含有总计两百多个详细注释的程序源代码与思路说明。读者可以按图索骥,按技术方向进行快速上手和深入学习。 本书要求读者具有基础的C/C++知识,适合研究计算机视觉以及相关领域的在校学生和老师、初次接触OpenCV 但有一定C/C++编程基础的研究人员,以及已有过OpenCV1.0编程经验,想快速了解并上手OpenCV2、OpenCV3编程的计算机视觉领域的专业人员。本书也适合于图像处理、计算机视觉领域的业余爱好者、开源项目爱好者做为通向新版 OpenCV的参考手册之用。
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摘 要 针对现有 Retinex 算法中存在的色彩失真、噪声放大及光晕伪影现象等问题, 本文提出了一种基于 Retinex 理论的 改进算法. 该算法首先在 HSV 空间对亮度分量 V 通道进行增强处理, 同时在拉伸得到的对数域反射分量至一定的动态范围 时 (本文是 0 ∼ 255), 引入增强调整因子, 调整不同亮度值的增强程度来避免噪声放大及色彩失真现象; 然后在 RGB 空间, 通 过分析光晕产生的原因, 提出一种改进的高斯滤波器来消除光晕现象, 并在计算反射分量时, 通过参数调整图像颜色的保真度. 最后, 对上述两种不同颜色空间的处理结果进行加权平均作为算法的最终输出. 实验结果表明, 针对不同光照条件下的图像, 1) 该算法可以明显地改善光晕伪影现象; 2) 无色彩失真、噪声放大等问题; 3) 效果和效率优于带色彩恢复的多尺度 Retinex 算法 (Multi-scale retinex with color restoration, MSRCR) 及其他对比算法.