add_noise_pinyutu

Historgram equilization, One Image to be added to the program for equilizzation.

主动轮廓模型图像分割算法，讲XU改进的算法用MATLAB进行实现(Active Contour Model for Image Segmentation Algorithm, speaking XU improved algorithm using MATLAB for the realization of)

contourlet 区域统计法，进行图像融合(contourlet regional statistical method for image fusion)

一个很好的学习图像处理的函数的例子程序，对于初级学习者来说，有很好的帮助，对于初学习者非常适合！(a good learning image processing function examples procedures for primary learners, good help, very suitable for early learners!)

SPIHT的图像压缩算法源代码。可以针对128×128的图像进行压缩(SPIHT image compression algorithm source code. 128 × 128 image compression)

数字图像处理常见问题及编程技巧汇编。希望对这方面的学习者有所帮助(Data, the software architect exam is a comprehensive commo)

说明：  根据文献Verification of color vegetation indices for automated crop imaging applications所述方法实现的可见光（RGB）的植被提取，效果非常好，压缩文件内附有该文献英文原文，以及部分供测试图片。(According to the method described in the document verification of color vegetation indexes for automated cross imaging applications, the effect of vegetation extraction of visible light (RGB) is very good. The original English version of the document and some test pictures are attached to the compressed file.)

下面给出了48项泽尼克多项式，外加一项常数项。需要注意的是，读者并不需要严格按照下文所示的顺序排列这些泽尼克项，实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项（piston term），这一项的系数也代表了平均光程差；而#1和#2项分别是x和y方向的倾斜项（tilt terms），#3代表了聚焦，因此，#1到#3项代表了波前的高斯或者近轴特性；#4和#5项代表了像散和离焦，#6和#7项代表彗差和倾斜，而#8项代表了3级像差和离焦，也就是说#4到#8项为3级相差项；同样地，#9到#15项代表了5级像差，而#16到#24项代表了7级像差，#25到#35项代表了9级像差，#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)

西电许录平数字图像处理课件及习题解析，部分实验(Digital Image Processing PPT)

视觉注意力模型，寻找感兴趣区域，模仿人眼找出最感兴趣的区域，再找出第二感兴趣的区域，以此类推(Visual attention model to find the region of interest, to imitate the human eye to identify the most interesting region, and then find the second region of interest, and so on)