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DSP包含的图像算法库

polly — Tue, 18 Sep 2012 11:19:00 GMT

摘要: 阅读全文

polly 2012-09-18 19:19 发表评论

【�{载】在Ubuntu下安装Python�U�学计算环境

polly — Tue, 18 Sep 2012 05:02:00 GMT

原文地址�Q?a target="_blank">在Ubuntu中安装Python�U�学计算环境 作�?/strong>�Q?a target="_blank">HyryStudio

在Ubuntu下安装Python模块通常可以使用apt-get和pip命��o。apt-get命��o是Ubuntu自带的包��理命��o�Q�而pip则是Python安装扩展模块的工��P��通常pip会下载扩展模块的源代码�ƈ�~�译安装�?/p>
Ubuntu 12.04中缺省安装了Python2.7.3�Q�首先通过下面的命令安装pip�Q�pip是Python的一个安装和��理扩展库的工具�?/p>

sudo apt-get install python-pip

安装Python开发环境，方便今后�~�译其他扩展库，占用�I�间92.8M�Q?/p>

sudo apt-get install python-dev

IPython

��Z��安装最新版的IPython 0.13beta�Q�需要下载IPython源代码，�q�执行安装命令。在IPython 0.13beta中提供了改进版本的IPython notebook。下面的命��o首先安装版本��理软�ggit�Q�然后通过git命��o从IPython的开发代码库中下载最新版本的IPython源代码，�q�执行安装命令：

cd
sudo apt-get install git
git clone https://github.com/ipython/ipython.git
cd ipython
sudo python setup.py install

如果安装目前的最新稳定版本，可以输入:

sudo apt-get install ipython

安装完毕之后�Q�请输入ipython命��o��试是否能正常启动�?/p>
��Z��让IPython notebook工作�Q�还�q�需要安装tornado和pyzmq�Q?/p>

sudo pip install tornado
sudo apt-get install libzmq-dev
sudo pip install pyzmq
sudo pip install pygments

下面��试IPython�Q?/p>

cd
mkdir notebook
cd notebook
ipython notebook

��Z��在IPython中离�U��用LaTeX数学公式�Q�需要安装mathjax�Q�首先输入下面的命��o启动ipython notebook�Q?/p>

sudo ipython notebook

在IPython notebook界面中输入：

from IPython.external.mathjax import install_mathjax
install_mathjax()

NumPy�Q�SciPy和matplotlib

通过apt-get命��o可以快速安装这三个库：

sudo apt-get install python-numpy
sudo apt-get install python-scipy
sudo apt-get install python-matplotlib

如果需要通过pip�~�译安装�Q�可以先用apt-get命��o安装所有编译所需的库�Q?/p>

sudo apt-get build-dep python-numpy
sudo apt-get build-dep python-scipy

然后通过pip命��o安装�Q?/p>

sudo pip install numpy
sudo pip install scipy

通过build-dep会安装很多库�Q�包括Python 3.2�?/div>

PyQt4和Spyder

下面的命令安装PyQt4�Q�Qt界面设计器，PyQt4的开发工具以及文档：

sudo apt-get install python-qt4
sudo apt-get install qt4-designer
sudo apt-get install pyqt4-dev-tools
sudo apt-get install python-qt4-doc

安装完毕之后�Q�文��位于：

/usr/share/doc/python-qt4-doc

安装好PyQt4之后通过下面的命令安装Spyder�Q?/p>

sudo apt-get install spyder

�׃��Spyder�l�常更新�Q�通过下面的命令可以安装最新版�Q?/p>

sudo pip install spyder --upgrade

cython和SWIG

Cython和SWIG是编写Python扩展模块的工��P��

sudo pip install cython
sudo apt-get install swig

输入 cython --version �?swig -version 查看版本�?/p>

ETS

ETS是enthought公司开发的一套科学计��Y件包�Q�其中的Mayavi通过VTK实现数据的三�l�可视化�?/p>
首先通过下面的命令安装编译ETS所需的库�Q?/p>

sudo apt-get install python-dev libxtst-dev scons python-vtk pyqt4-dev-tools python2.7-wxgtk2.8 python-configobj
sudo apt-get install libgl1-mesa-dev libglu1-mesa-dev

创徏ets目录�Q��ƈ在此目录下下载ets.py�Q�运行ets.py可以复制最新版的ETS源程序，�q�安装：

mkdir ets
cd ets
wget https://github.com/enthought/ets/raw/master/ets.py
python ets.py clone
sudo python ets.py develop
#sudo python ets.py install 或者运行install安装

如果一切正常，那么输入 mayavi2 命��o则会启动mayavi�?/p>

OpenCV

��Z��~�译OpenCV需要下载cmake�~�译工具�Q�和一些依赖库�Q?/p>

sudo apt-get install build-essential
sudo apt-get install cmake
sudo apt-get install cmake-gui
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libjpeg-dev libpng-dev libtiff-dev libjasper-dev

然后�?http://sourceforge.net/projects/opencvlibrary/ 下蝲最新版的OpenCV源代码，�q�解压。然后创建编译用的目录release�Q��ƈ启动cmake-gui:

mkdir release
cmake-gui

在界面中选择OpenCV源代码的目录�Q�和�~�译输出目录release�Q�然后按Configure按钮�Q��ƈ�Ҏ��需要设�|�各个编译选项�Q�最后点Generate按钮�Q�退出cmake-gui界面。进入编译�\径，执行下面的命令：

cd release
make
sudo make install

安装完毕之后�Q�启动IPython�Q��ƈ输入 import cv2 ��试OpenCV是否能正常蝲入�?/p>

polly 2012-09-18 13:02 发表评论

polly — Mon, 17 Sep 2012 06:20:00 GMT

polly 2012-09-17 14:20 发表评论

��国航空母舰�?Google Earth上的坐标

polly — Sun, 19 Aug 2012 02:34:00 GMT

Google Earth坐标�Q�美国航�I�母舰坐�?/h2>

　　�q�里�|�列了已�l�发现的所有美国现役和退役的航空母舰。其中包括：

　　“��鹰”�?CV63　 35°17'29.66"N,139°39'43.67"E

　　“肯尼�q?#8221;�?CVN67　 30°23'50.91"N, 81°24'14.86"W

　　“��米�?#8221;�?CVN68　 32°42'47.88"N,117°11'22.49"W

　　“艾森豪威��?#8221;�?CVN69　 36°57'27.13"N, 76°19'46.35"W

　　“林肯” �?CVN72 　 47°58'53.54"N,122°13'42.94"W

　　“华盛��?#8221;�?CVN73　 36°57'32.90"N, 76°19'45.10"W

　　“杜鲁�?#8221;�?CVN75　　36°48'53.25"N,76°17'49.29"W

　　“无畏”�?CV-11　　 40°45'53.88"N,74° 0'4.22"W

　　“莱克星顿”�?CV-2　　27°48'54.13"N,97°23'19.65"W

　　“星��”�?47°33'11.30"N,122°39'17.24"W

　　“独立”�?47°33'7.53"N,122°39'30.13"W

　　“渔R��?#8221;�?47°33'10.63"N,122°39'9.53"W

　　“佛瑞斯特”号和“萨拉托加”受��41°31'39.59"N,71°18'58.70"W

　　“��利�?#8221;受��39°53'6.36"N,75°10'45.55"W

polly 2012-08-19 10:34 发表评论

��国��军航空母舰列表

polly — Sun, 19 Aug 2012 02:33:00 GMT

本列表收录了��国��军己退�Ҏ��现役中的航空母舰�Q�包�?a class="mw-redirect" title="��国��军��C��~�号" >船��属于CV、CVA、CVB、CVL或CVN的全部舰只。编号在CVA-58之后的都属于��航空母舰�Q?a title="排水�? >排水�?/a>��过75,000吨）�Q�CVN-65和CVN-68以后的都属于核动力航�I�母�?/a>�?/p>
排水量较��的护航航空母舰�Q�Escort Aircraft Carriers�Q�CVE�Q�，则另行收录于��国��军护航航空母舰列表中�?/p>

船舰�~�号舰名 �U�别附注

CV-1 Langley 兰利�?/a> 以运煤舰朱比特号�Q�USS Jupiter�Q�改造而成

CV-2 Lexington 列克星敦�?/a> 5�?�?/a>CV-3 Saratoga 萨拉托加�?/a> 列克星敦�U?/td> 7�?5�?/a>�?a title="比基��环�C? >比基��环�C?/a>的核子武器试验中沉没

CV-4 Ranger �H�击者号 �H�击者�� 10�?8�?/a>退�?/td>

CV-5 Yorktown �U�克城号 �U�克城�� 6�?�?/a>�?a title="中途岛��h��" >中途岛��h��中沉�?/td>

CV-6 Enterprise 企业�?/a> �U�克城�� 2�?7�?/a>退�?/td>

CV-7 Wasp 胡蜂�?/a> 9�?5�?/a>�?a class="mw-redirect" title="日军" >日军潜艇��L��

CV-8 Hornet 大黄蜂号 �U�克城�� 10�?7�?/a>�?a title="圣克鲁斯��岛战役" >圣克鲁斯��岛战役中受重创沉没

CV-9 Essex 6�?0�?/a>退�?/td>

CV-10 Yorktown �U�克城号埃塞克斯�U?/td> 6�?7�?/a>退�?/td>

CV-11 Intrepid 埃塞克斯�U?/td> 3�?5�?/a>退�?/td>

CV-12 Hornet 大黄蜂号埃塞克斯�U?/td> 6�?4�?/a>退�?/td>

CV-13 Franklin 埃塞克斯�U?/td> 2�?7�?/a>退�?/td>

CV-14 Ticonderoga 提康��L��加号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-15 Randolph 伦道夫号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-16 Lexington 列克星敦�?/a> 埃塞克斯�U?/td> 11�?�?/a>退�?/td>

CV-17 Bunker Hill 邦克山号埃塞克斯�U?/td> 1�?�?/a>退�?/td>

CV-18 Wasp 胡蜂�?/a> 埃塞克斯�U?/td> 7�?�?/a>退�?/td>

CV-19 Hancock 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-20 Bennington 本宁��号埃塞克斯�U?/td> 1�?5�?/a>退�?/td>

CV-21 Boxer 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CVL-22 Independence 独立�?/a> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-23 Princeton 普林斯顿�?/a> 独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-24 Belleau Wood 独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-25 Cowpens �U�本斯号独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-26 Monterey 蒙特利号独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-27 Langley 兰利�?/a> 独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-28 Cabot 卡伯特号独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-29 Bataan 独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CVL-30 San Jacinto 独立�U?/td> �?#8220;克里夫兰�U�轻巡洋�?#8221;改装而成

CV-31 Bon Homme Richard 好�h理查德号埃塞克斯�U?/td> 7�?�?/a>退�?/td>

CV-32 Leyte 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-33 Kearsarge 奇沙��d�� 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-34 Oriskany 奥里斯卡��号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-35 Reprisal 埃塞克斯�U?/td> 建造中途取�?/td>

CV-36 Antietam 安提坦号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-37 Princeton 普林斯顿�?/a> 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-38 Shangri-la 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-39 Lake Champlain 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-40 Tarawa 塔拉瓦号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CVB-41 Midway 中途岛�U?/a> 4�?1�?/a>退�?/td>

CVB-42 Franklin D. Roosevelt 中途岛�U?/td>

CVB-43 Coral Sea 中途岛�U?/td>

CVB-44 �?/td> 建造计划取�?/td>

CV-45 Valley Forge ��吉谷号埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CV-46 Iwo Jima ��磺岛号埃塞克斯�U?/td> 建造计划取�?/td>

CV-47 Philippine Sea 埃塞克斯�U?/td> 长舰体埃塞克斯��Q�Long-hull Essex�Q?/td>

CVL-48 Saipan 1�?4�?/a> 正式除役

CVL-49 Wright ��q��?/a> 塞班岛�� 5�?7�?/a> 正式除役

CV-50到CV-55 �?/td> 埃塞克斯�U?/td> 建造计划取�?/td>

CVB-56到CVB-57 �?/td> 中途岛�U?/td> 建造中途取�?/td>

CVA-58 United States 建造中途取�?/td>

CVA-59 Forrestal ��莱斯特�U?/a> 9�?1�?/a> 正式除役

CVA-60 Saratoga ��莱斯特�U?/td> 8�?0�?/a> 正式除役

CVA-61 Ranger ��莱斯特�U?/td> 7�?0�?/a> 正式除役

CV-62 Independence ��莱斯特�U?/td> 9�?0�?/a> 正式除役

CV-63 Kitty Hawk 5�?2�?/a> 正式除役

CV-64 Constellation ��鹰�U?/td> 8�?�?/a> 正式除役

CVN-65 Enterprise 企业�?/a> 企业�U?/td> 服役�?/td>

CVA-66 America ��鹰�U?/td> 8�?�?/a> 正式除役

CV-67 John F. Kennedy �Q�改良）��鹰�U?/td> 8�?�?/a> 正式除役

CVN-68 Nimitz ��米兹号服役�?/td>

CVN-69 Dwight D. Eisenhower 艾森豪威��号 ��米兹�� 服役�?/td>

CVN-70 Carl Vinson ��米兹�� 服役�?/td>

CVN-71 Theodore Roosevelt �|�斯��号 ��米兹�� 服役�?/td>

CVN-72 Abraham Lincoln ��米兹�� 服役�?/td>

CVN-73 George Washington 华盛��号 ��米兹�� 服役�?/td>

CVN-74 John C. Stennis ��米兹�� 服役�?/td>

CVN-75 Harry S. Truman 杜鲁门号 ��米兹�� 服役�?/td>

CVN-76 Ronald Reagan ��米兹�� 服役�?/td>

CVN-77 George H. W. Bush ��米兹�� 服役�?/td>

CVN-78 Gerald R. Ford 建造中

CVN-79 John F. Kennedy 肯尼�q�号 ��特�U?/td> 建造中

CVN-80 未命�?/a> ��特�U?/td> 计划�?/td>

polly 2012-08-19 10:33 发表评论

polly — Fri, 10 Aug 2012 02:42:00 GMT

高光谱成像是��C��代光甉|��技术，兴�v�?O世纪8O�q�代�Q�目前仍在迅猛发展巾。高光谱成像是相对多光谱成像而言�Q�通过高光谱成像方法获得的高光谱图像与通过多光谱成像获取的多光谱图像相比具有更丰富的图像和光谱信息。如果根据传感器的光谱分辨率对光谱成像技术进行分�c�，光谱成像技术一般可分成3�c�R�?/p>

(1) 多光谱成�?#8212;—光谱分��L率在 delta_lambda/lambda=0�Q?数量�U�，�q�样的传感器在可见光和近�U�外区域一般只有几个�L�D�c�?/p>

(2) 高光谱成�?#8212;— 光谱分��L率在 delta_lambda/lambda=0�Q?1数量�U�，�q�样的传感器在可见光和近�U�外区域有几卜到数百个�L�D�，光谱分��L率可达nm�U��?/p>

(3) ��光谱成�?#8212;— 光谱分��L率在delta_lambda/lambda =O�Q?01数量�U�，�q�样的传感器在可见光和近�U�外区域可达数千个�L�D�c�?/p>

众所周知�Q�光谱分析是自然�U�学中一�U�重要的研究手段�Q�光谱技术能��到被测物体的物理结构、化学成分等指标。光��p��h��Z��Ҏ��量，而图像测量是��Z��I�间�Ҏ��变化，两者各有其优缺炏V��因此，可以说光谱成像技术是光谱分析技术和囑փ�分析技术发展的必然�l�果�Q�是二者完��结合的产物。光谱成像技术不仅具有光谱分辨能力，�q�具有图像分辨能力，利用光谱成像技术不仅可以对待检��物体进行定性和定量分析�Q�而且�q�能�q�对其进行定位分析�?/p>

高光谱成像系�l�的主要工作部�g是成像光�׃�A�Q�它是一�U�新型传感器�Q?O世纪8O�q�代初正式开始研�Ӟ��研制�q�类仪器的目的是��取大量窄波段�q�箋光谱囑փ�数据�Q��每个像元��h��几乎�q�箋的光谱数据。它是一�p�d��光�L波长处的光学囑փ��Q�通常包含数十到数百个波段�Q�光谱分辨率一般�ؓ1～l0nm。由于高光谱成像所获得的高光谱囑փ�能对囑փ�中的每个像素提供一条几乎连�l�的光谱曲线�Q�其在待��物上获得空间信息的同时又能获得比多光谱更�ؓ丰富光谱数据信息�Q�这些数据信息可用来生成复杂模型�Q�来�q�行判别、分�c�R��识别图像中的材料�?/p>

通过高光谱成像获取待��物的高光谱囑փ�包含了待��物的丰富的�I�间、光谱和辐射三重信息。这些信息不仅表��C��

地物�I�间分布的媄像特征，同时也可能以其中某一像元或像元组为目标获取它们的辐射强度以及光谱特征。媄像、辐��与光谱是高光谱囑փ�中的3个重要特征，�q?个特征的有机�l�合��是高光谱图像�?/p>

高光谱图像数据�ؓ数据立方�?cube)。通常囑փ�像素的横坐标和纵坐标分别用z和Y来表�C�，光谱的�L长信息以(Z卌��u)表示。该数据立方体由沿着光谱轴的以一定光谱分辨率间隔的连�l�二�l�图像组成�?/p>

polly 2012-08-10 10:42 发表评论

vs + opencv 常见错误

polly — Mon, 30 Jul 2012 03:57:00 GMT
Q:Link Error 2019 无法解析的外部符�?nbsp; _cvCreateImage
A:应将解决�Ҏ��q�_��改�ؓwin64�?br />工具栏上方的解决�Ҏ��q�_��—》点��M��拉菜�?#8212;》配�|�管理器—》活动解��x��案��^�?#8212;》新�?#8212;》键入获选着新��^�?#8212;》x64
问题��p��军_��Q�哈哈！

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polly 2012-07-30 11:57 发表评论

polly — Wed, 25 Jul 2012 11:02:00 GMT
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polly 2012-07-25 19:02 发表评论

Bilateral Filtering for Gray and Color Images

polly — Tue, 24 Jul 2012 12:39:00 GMT

Introduction
The Idea
The Gaussian Case
Experiments with Black-and-White Images
Experiments with Color Images
References

Introduction

Filtering is perhaps the most fundamental operation of image processing and computer vision. In the broadest sense of the term "filtering", the value of the filtered image at a given location is a function of the values of the input image in a small neighborhood of the same location. For example, Gaussian low-pass filtering computes a weighted average of pixel values in the neighborhood, in which the weights decrease with distance from the neighborhood center. Although formal and quantitative explanations of this weight fall-off can be given, the intuition is that images typically vary slowly over space, so near pixels are likely to have similar values, and it is therefore appropriate to average them together. The noise values that corrupt these nearby pixels are mutually less correlated than the signal values, so noise is averaged away while signal is preserved.
The assumption of slow spatial variations fails at edges, which are consequently blurred by linear low-pass filtering. How can we prevent averaging across edges, while still averaging within smooth regions? Many efforts have been devoted to reducing this undesired effect. Bilateral filtering is a simple, non-iterative scheme for edge-preserving smoothing.

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The Idea

The basic idea underlying bilateral filtering is to do in the range of an image what traditional filters do in its domain. Two pixels can be close to one another, that is, occupy nearby spatial location, or they can be similar to one another, that is, have nearby values, possibly in a perceptually meaningful fashion.
Consider a shift-invariant low-pass domain filter applied to an image:

The bold font for f and h emphasizes the fact that both input and output images may be multi-band. In order to preserve the DC component, it must be

Range filtering is similarly defined:

In this case, the kernel measures the photometric similarity between pixels. The normalization constant in this case is

The spatial distribution of image intensities plays no role in range filtering taken by itself. Combining intensities from the entire image, however, makes little sense, since the distribution of image values far away from x ought not to affect the final value at x. In addition, one can show that range filtering without domain filtering merely changes the color map of an image, and is therefore of little use. The appropriate solution is to combine domain and range filtering, thereby enforcing both geometric and photometric locality. Combined filtering can be described as follows:

with the normalization

Combined domain and range filtering will be denoted as bilateral filtering. It replaces the pixel value at x with an average of similar and nearby pixel values. In smooth regions, pixel values in a small neighborhood are similar to each other, and the bilateral filter acts essentially as a standard domain filter, averaging away the small, weakly correlated differences between pixel values caused by noise. Consider now a sharp boundary between a dark and a bright region, as in figure 1(a).

(a)

(b)

(c)

Figure 1

When the bilateral filter is centered, say, on a pixel on the bright side of the boundary, the similarity function s assumes values close to one for pixels on the same side, and values close to zero for pixels on the dark side. The similarity function is shown in figure 1(b) for a 23x23 filter support centered two pixels to the right of the step in figure 1(a). The normalization term k(x) ensures that the weights for all the pixels add up to one. As a result, the filter replaces the bright pixel at the center by an average of the bright pixels in its vicinity, and essentially ignores the dark pixels. Conversely, when the filter is centered on a dark pixel, the bright pixels are ignored instead. Thus, as shown in figure 1(c), good filtering behavior is achieved at the boundaries, thanks to the domain component of the filter, and crisp edges are preserved at the same time, thanks to the range component.

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The Gaussian Case

A simple and important case of bilateral filtering is shift-invariant Gaussian filtering, in which both the closeness function c and the similarity function s are Gaussian functions of the Euclidean distance between their arguments. More specifically, c is radially symmetric:

where

is the Euclidean distance. The similarity function s is perfectly analogous to c :

where

is a suitable measure of distance in intensity space. In the scalar case, this may be simply the absolute difference of the pixel difference or, since noise increases with image intensity, an intensity-dependent version of it. Just as this form of domain filtering is shift-invariant, the Gaussian range filter introduced above is insensitive to overall additive changes of image intensity. Of course, the range filter is shift-invariant as well.

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Experiments with Black-and-White Images

Figure 2 (a) and (b) show the potential of bilateral filtering for the removal of texture. The picture "simplification" illustrated by figure 2 (b) can be useful for data reduction without loss of overall shape features in applications such as image transmission, picture editing and manipulation, image description for retrieval.

(a)

(b)

Figure 2

Bilateral filtering with parameters sd =3 pixels and sr =50 intensity values is applied to the image in figure 3 (a) to yield the image in figure 3 (b). Notice that most of the fine texture has been filtered away, and yet all contours are as crisp as in the original image. Figure 3 (c) shows a detail of figure 3 (a), and figure 3 (d) shows the corresponding filtered version. The two onions have assumed a graphics-like appearance, and the fine texture has gone. However, the overall shading is preserved, because it is well within the band of the domain filter and is almost unaffected by the range filter. Also, the boundaries of the onions are preserved.

(a)

(b)

(c)

(d)

Figure 3

Back to Index

Experiments with Color Images

For black-and-white images, intensities between any two gray levels are still gray levels. As a consequence, when smoothing black-and-white images with a standard low-pass filter, intermediate levels of gray are produced across edges, thereby producing blurred images. With color images, an additional complication arises from the fact that between any two colors there are other, often rather different colors. For instance, between blue and red there are various shades of pink and purple. Thus, disturbing color bands may be produced when smoothing across color edges. The smoothed image does not just look blurred, it also exhibits odd-looking, colored auras around objects.

(a)

(b)

(c)

(d)

Figure 4

Figure 4 (a) shows a detail from a picture with a red jacket against a blue sky. Even in this unblurred picture, a thin pink-purple line is visible, and is caused by a combination of lens blurring and pixel averaging. In fact, pixels along the boundary, when projected back into the scene, intersect both red jacket and blue sky, and the resulting color is the pink average of red and blue. When smoothing, this effect is emphasized, as the broad, blurred pink-purple area in figure 4 (b) shows.
To address this difficulty, edge-preserving smoothing could be applied to the red, green, and blue components of the image separately. However, the intensity profiles across the edge in the three color bands are in general different. Smoothing the three color bands separately results in an even more pronounced pink and purple band than in the original, as shown in figure 4 (c). The pink-purple band, however, is not widened as in the standard-blurred version of figure 4 (b).
A much better result can be obtained with bilateral filtering. In fact, a bilateral filter allows combining the three color bands appropriately, and measuring photometric distances between pixels in the combined space. Moreover, this combined distance can be made to correspond closely to perceived dissimilarity by using Euclidean distance in the CIE-Lab color space. This color space is based on a large body of psychophysical data concerning color-matching experiments performed by human observers. In this space, small Euclidean distances are designed to correlate strongly with the perception of color discrepancy as experienced by an "average" color-normal human observer. Thus, in a sense, bilateral filtering performed in the CIE-Lab color space is the most natural type of filtering for color images: only perceptually similar colors are averaged together, and only perceptually important edges are preserved. Figure 4 (d) shows the image resulting from bilateral smoothing of the image in figure 4 (a). The pink band has shrunk considerably, and no extraneous colors appear.

(a)

(b)

(c)

Figure 5

Figure 5 (c) shows the result of five iterations of bilateral filtering of the image in figure 5 (a). While a single iteration produces a much cleaner image (figure 5 (b)) than the original, and is probably sufficient for most image processing needs, multiple iterations have the effect of flattening the colors in an image considerably, but without blurring edges. The resulting image has a much smaller color map, and the effects of bilateral filtering are easier to see when displayed on a printed page. Notice the cartoon-like appearance of figure 5 (c). All shadows and edges are preserved, but most of the shading is gone, and no "new" colors are introduced by filtering.

Back to Index

References

[1] C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India.
[2] T. Boult, R.A. Melter, F. Skorina, and I. Stojmenovic,"G-neighbors", Proceedings of the SPIE Conference on Vision Geometry II, pages 96-109, 1993.
[3] R.T. Chin and C.L. Yeh, "Quantitative evaluation of some edge-preserving noise-smoothing techniques", Computer Vision, Graphics, and Image Processing, 23:67-91, 1983.
[4] L.S. Davis and A. Rosenfeld, "Noise cleaning by iterated local averaging", IEEE Transactions on Systems, Man, and Cybernetics, 8:705-710, 1978.
[5] R.E. Graham, "Snow-removal - a noise-stripping process for picture signals", IRE Transactions on Information Theory, 8:129-144, 1961.
[6] N. Himayat and S.A. Kassam, "Approximate performance analysis of edge preserving filters", IEEE Transactions on Signal Processing, 41(9):2764-77, 1993.
[7] T.S. Huang, G.J. Yang, and G.Y. Tang, "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1):13-18, 1979.
[8] J.S. Lee, "Digital image enhancement and noise filtering by use of local statistics", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(2):165-168, 1980.
[9] M. Nagao and T. Matsuyama, "Edge preserving smoothing", Computer Graphics and Image Processing, 9:394-407, 1979.
[10] P.M. Narendra, "A separable median filter for image noise smoothing", IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(1):20-29, 1981.
[11] K.J. Overton and T.E. Weymouth, "A noise reducing preprocessing algorithm",Proceedings of the IEEE Computer Science Conference on Pattern Recognition and Image Processing, pages 498-507, Chicago, IL, 1979.
[12] P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639, 1990.
[13] G. Ramponi, "A rational edge-preserving smoother", Proceedings of the International Conference on Image Processing, volume 1, pages 151-154, Washington, DC, 1995.
[14] G. Sapiro and D.L. Ringach, "Anisotropic diffusion of color images", Proceedings of the SPIE, volume 2657, pages 471-382, 1996.
[15] D.C.C. Wang, A.H. Vagnucci, and C.C. Li, "A gradient inverse weighted smoothing scheme and the evaluation of its performance", Computer Vision, Graphics, and Image Processing, 15:167-181, 1981.
[16] G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae, John Wiley and Sons, New York, NY, 1982.
[17] L. Yin, R. Yang, M. Gabbouj, and Y. Neuvo, "Weighted median filters: a tutorial",IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 43(3):155-192, 1996.

polly 2012-07-24 20:39 发表评论

polly — Tue, 24 Jul 2012 12:34:00 GMT

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polly 2012-07-24 20:34 发表评论

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Bilateral Filtering for Gray and Color Images

Introduction

The Idea

The Gaussian Case

Experiments with Black-and-White Images

Experiments with Color Images

References

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