数字图像处理算法.doc

窗体顶端摘要: 关于空间域图像处理算法框架，直方图处理，空间域滤波器算法框架的编程心得，使用GDI+(C+)一，图像文件的读取 初学数字图像处理时，图像文件的读取往往是一件麻烦的事情，我们要面对各种各样的图像文件格式，如果仅用C+的fstream库那就必须了解各种图像编码格式，这对于初学图像处理是不太现实的，需要一个能帮助轻松读取各类图像文件的库。在Win32平台上GDI+(C+)是不错的选择，不光使用上相对于Win32 GDI要容易得多，而且也容易移植到.Net平台上的GDI+。 Gdiplus:Bitmap类为我们提供了读取各类图像文件的接口，Bitmap:LockBits方法产生的BitmapData类也为我们提供了高速访问图像文件流的途径。这样我们就可以将精力集中于图像处理算法的实现，而不用关心各种图像编码。具体使用方式请参考MSDN中GDI+文档中关于Bitmap类和BitmapData类的说明。另外GDI+仅在Windows XP/2003上获得直接支持，对于Windows 2000必须安装相关DLL，或者安装有Office 2003，Visual Studio 2003 .Net等软件。二，空间域图像处理算法框架(1) 在空间域图像处理中，对于一个图像我们往往需要对其逐个像素的进行处理，对每个像素的处理使用相同的算法（或者是图像中的某个矩形部分）。即，对于图像f(x,y),其中0xM,0yN,图像为M*N大小，使用算法algo，则f(x,y) = algo(f(x,y)。事先实现一个算法框架，然后再以函数指针或函数对象(functor,即实现operator()的对象)传入算法，可以减轻编程的工作量。 如下代码便是一例:#ifndef PROCESSALGO_H#define PROCESSALGO_H#include #include namespace nsimgtk template bool ProcessPixelsOneByOne(Gdiplus:Bitmap* const p_bitmap, Processor processor, unsigned int x, unsigned int y, unsigned int width, unsigned int height) if (p_bitmap = NULL) return false; if (width + x p_bitmap-GetWidth() | (height + y p_bitmap-GetHeight() return false; Gdiplus:BitmapData bitmapData; Gdiplus:Rect rect(x, y, width,height); if (p_bitmap-LockBits(&rect, Gdiplus:ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus:Ok) return false; pixelType *pixels = (pixelType*)bitmapData.Scan0; for (unsigned int row=0; rowheight; +row) for (unsigned int col=0; colUnlockBits(&bitmapData) != Gdiplus:Ok) return false; return true; #endifProcessPixelsOneByOne函数可以对图像中从(x,y)位置起始，width*height大小的区域进行处理。模板参数pixelType用于指定像素大小，例如在Win32平台上传入unsigned char即为8位，用于8阶灰度图。模板参数Processor为图像处理算法实现，可以定义类实现void operator(pixelType *)函数，或者传入同样接口的函数指针。 如下便是一些算法示例(说明见具体注释):#ifndef SPATIALDOMAIN_H#define SPATIALDOMAIN_H#include #include namespace nsimgtk/ 8阶灰度图的灰度反转算法 class NegativeGray8 public: void operator()(unsigned char *const p_value) *p_value = 0xff; ; / 8阶灰度图的Gamma校正算法 class GammaCorrectGray8 private: unsigned char d_s256; public: GammaCorrectGray8:GammaCorrectGray8(double c, double gamma); void operator()(unsigned char*const p_value) *p_value = d_s*p_value; ; / 8阶灰度图的饱和度拉伸算法 class ContrastStretchingGray8 private: unsigned char d_s256; public: ContrastStretchingGray8:ContrastStretchingGray8(double a1, double b1, unsigned int x1, double a2, double b2, unsigned int x2, double a3, double b3); void operator()(unsigned char*const p_value) *p_value = d_s*p_value; ; / 8阶灰度图的位平面分割，构造函数指定位平面号 class BitPlaneSliceGray8 private: unsigned char d_s256; public: BitPlaneSliceGray8(unsigned char bitPlaneNum); void operator()(unsigned char* const p_value) *p_value = d_s*p_value; ;#endif/ 上述类中各构造函数的实现代码，应该分在另一个文件中，此处为说明方便，一并列出#include SpatialDomain/spatialDomain.hnamespace nsimgtk GammaCorrectGray8:GammaCorrectGray8(double c, double gamma) double temp; for (unsigned int i=0; i 255 | x2 255 | x1 x1) for (unsigned int i=0; i256; +i) d_si = i; else double tmp; for (unsigned int i=0; ix1; +i) tmp = ceil(a1*double(i)+b1); d_si = (unsigned char)tmp; for (unsigned int i=x1; ix2; +i) tmp = ceil(a2*double(i)+b2); d_si = (unsigned char)tmp; for (unsigned int i=x2; i256; +i) tmp = ceil(a3*double(i)+b3); d_si = (unsigned char)tmp; BitPlaneSliceGray8:BitPlaneSliceGray8(unsigned char bitPlaneNum) unsigned char bitMaskArray8 = 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80 ; for (unsigned int i=0; i bitPlaneNum) * 255; d_si = tmp; (2) 直方图在GDI+1.0中没有获得支持，我们必须自行实现。直方图相关的处理在数字图像处理中占有重要地位，可以通过它获取图像灰度级的统计信息，且可以通过直方图进行一些重要的图像增强技术，如直方图均衡化，直方图规定化，基本全局门限等。下面是获取8阶图像直方图的算法实现：namespace nsimgtk bool GetHistogramNormalizeGray8(Gdiplus:Bitmap * const p_bitmap, float *histogramArray) if (p_bitmap = NULL | histogramArray = NULL) return false; Gdiplus:BitmapData bitmapData; Gdiplus:Rect rect(0, 0, p_bitmap-GetWidth(), p_bitmap-GetHeight(); if (p_bitmap-LockBits(&rect, Gdiplus:ImageLockModeRead, PixelFormat8bppIndexed, &bitmapData) != Gdiplus:Ok) return false; unsigned char *pixels = (unsigned char*)bitmapData.Scan0; unsigned int histogram256; for (int i=0; i256; +i) histogrami = 0; for (unsigned int row=0; rowGetWidth(); +row) for (unsigned int col=0; colGetHeight(); +col) +histogrampixelscol+row*bitmapData.Stride; const unsigned int totalPixels = p_bitmap-GetWidth() * p_bitmap-GetHeight(); for (int i=0; iUnlockBits(&bitmapData) != Gdiplus:Ok) return false; return true; 在获取直方图后(即上面算法的第二个参数)，再将其作为参数传入下面的对象的构造函数，然后以该对象为仿函数传入ProcessPixelsOneByOne即可实现8阶图像直方图均衡化:#ifndef SPATIALDOMAIN_H#define SPATIALDOMAIN_H#include #include namespace nsimgtk / 8阶灰度图的直方图均衡化 class HistogramEqualizationGray8 private: unsigned char d_s256; public: HistogramEqualizationGray8(const float *const histogramArray); void operator()(unsigned char *const p_value) *p_value = d_s*p_value; ;#endif /#include SpatialDomain/spatialDomain.hnamespace nsimgtk HistogramEqualizationGray8:HistogramEqualizationGray8(const float *const histogramArray) if (histogramArray != NULL) float sum = 0.0; for (int i=0; i256; +i) sum += histogramArrayi; d_si = unsigned char(sum * 255); (3)空间域滤波器，滤波器是一个m*n大小的掩模，其中m,n均为大于1的奇数。滤波器逐像素地通过图像的全部或部分矩形区域，然后逐像素地对掩模覆盖下的像素使用滤波器算法获得响应，将响应赋值于当前像素即掩模中心像素，另外滤波器算法使用中将会涉及到图像边缘的问题，这可以对边缘部分掩模使用补零法补齐掩模下无像素值的区域，或者掩模的移动范围以不越出图像边缘的方式移动，当然这些处理方法都会给图像边缘部分带来不良效果，但是一般情况下，图像边缘部分往往不是我们关注的部分或者没有重要的信息。下面的滤波器算法框架SpatialFilterAlgo即以补零法(zero-padding)实现:#ifndef SPATIALFILTER_H#define SPATIALFILTER_H#include #include #include #include #include #include namespace nsimgtk template bool SpatialFilterAlgo(Gdiplus:Bitmap* const p_bitmap, FilterMask filterMask, unsigned int x, unsigned int y, unsigned int width, unsigned int height) if (p_bitmap = NULL) return false; if (width + x p_bitmap-GetWidth() | (height + y p_bitmap-GetHeight() return false; Gdiplus:BitmapData bitmapData; Gdiplus:Rect rect(x, y, width,height); if (p_bitmap-LockBits(&rect, Gdiplus:ImageLockModeWrite, pixelFormat, &bitmapData) != Gdiplus:Ok) return false; pixelType *pixels = (pixelType*)bitmapData.Scan0; const unsigned int m = filterMask.d_m; / masks width const unsigned int n = filterMask.d_n; / masks height std:vector tmpImage(m-1+width)*(n-1+height); / extend image to use zero-padding / copy original bitmap to extended image with zero-padding method for (unsigned int row=0; rowheight; +row) for (unsigned int col=0; colwidth; +col) tmpImage(col+m/2)+(row+n/2)*(bitmapData.Stride/sizeof(pixelType)+m-1) = pixelscol+row*bitmapData.Stride/sizeof(pixelType); / process every pixel with filterMask for (unsigned int row=0; rowheight; +row) for (unsigned int col=0; colwidth; +col) / fill the m*n mask with the current pixels neighborhood for (unsigned int i=0; in; +i) for (unsigned int j=0; jUnlockBits(&bitmapData) != Gdiplus:Ok) return false; return true; #endif其中模板参数FilterMask即为滤波掩模算法。通常的滤波算法有均值滤波器，可以模糊化图像，去除图形中的细节部分，使得我们可以关注图像中较为明显的部分，均值滤波器用于周期性噪声。中值滤波器用于图像中存在椒盐噪声也即脉冲噪声的情况下。另外有基于一阶微分的Sobel梯度算子和基于两阶微分的拉普拉斯算子，它们往往被用于边缘检测中。下面是一些滤波器算法的具体实现，所以滤波器算法都应该实现pixelType response()函数以及有d_mask,d_m,d_n成员，这可以通过继承_filteMask类获得(不需要付出虚函数代价)。#ifndef SPATIALFILTER_H#define SPATIALFILTER_H#include #include #include #include #include #include namespace nsimgtk / 滤波器掩模的基类，提供掩模大小d_m,d_n，掩模覆盖下的m*n个像素值d_mask / others filterMask should inherit it template struct _filterMask const unsigned int d_m; const unsigned int d_n; / images pixels under the m*n filter mask std:vector d_mask; / filter masks width and heigh must be a odd, if not, it will plus one for the width or the height _filterMask(unsigned int m, unsigned int n) : d_m(m%2 ? m:m+1), d_n(n%2 ? n:n+1), d_mask(d_m*d_n) ; / 掩模权值为全1的均值滤波器 template class averagingFilterMaskSp : public _filterMask public: averagingFilterMaskSp(unsigned int m, unsigned int n) : _filterMask(m, n) pixelType response() return std:accumulate(d_mask.begin(), d_mask.end(), 0) / (d_m * d_n); ; / 可自定义掩模权值的均值滤波器 template class averagingFilterMask : public _filterMask private: std:vector d_weight; / weights vector(m*n) int d_weight_sum; / all weights sum public: averagingFilterMask(unsigned int m, unsigned int n, const std:vector& weightVec) : _filterMask(m, n), d_weight(weightVec) if (weightVec.size() != d_mask.size() / if weights size isnt equal to masks size, it will change filter mask as a special filter mask d_weight.resize(d_mask.size(), 1); d_weight_sum = std:accumulate(d_weight.begin(), d_weight.end(), 0); pixelType response() return std:inner_product(d_mask.begin(), d_mask.end(), d_weight.begin(), 0) / d_weight_sum; ; / 中值滤波器 template class medianFilterMask : public _filterMask public: medianFilterMask(unsigned int m, unsigned int n) : _filterMask(m, n) pixelType response() std:sort(d_mask.begin(), d_mask.end(); return d_maskd_mask.size()/2; ; / 3*3拉普拉斯滤波器 / the mask is:0 1 0 0 -1 0 / 1 -5 1 or -1 5 -1 / 0 1 0 0 -1 0 / if pixels brightness is less than min, set it to min / if pixels brightness is larger than max, set it to max template class laplacianFilter : public _filterMask public: laplacianFilter() : _filterMask(3, 3) pixelType response() int ret = (int)(5*(int)d_mask4) -(int)d_mask5+d_mask3+d_mask1+d_mask7); if (ret max) ret = max; return ret; ; / 3*3Sobel滤波器 / the mask is: -1 -2 -1 -1 0 1 / 0 0 0 and -2 0 2 / 1 2 1 -1 0 1 / if pixels brightness is larger than max, set it to max template class sobelFilter : public _filterMask public: sobelFilter() : _filterMask(3, 3) pixelType response() int ret = :abs(d_mask6+2*d_mask7+d_mask8-d_mask0-2*d_mask1-d_mask2) + :abs(d_mask2+2*d_mask5+d_mask8-d_mask0-2*d_mask3-d_mask6); if (ret max) ret = max; return ret; ;#endif数字图像处理算法实现 -编程心得(1)2001414班朱伟 20014123摘要: 关于空间域图像处理算法框架，直方图处理，空间域滤波器算法框架的编程心得，使用GDI+(C+)一，图像文件的读取 初学数字图像处理时，图像文件的读取往往是一件麻烦的事情，我们要面对各种各样的图像文件格式，如果仅用C+的fstream库那就必须了解各种图像编码格式，这对于初学图像处理是不太现实的，