图像卷积回顾与计算优化(待续)

卷积及其计算方法是《信号与系统》与《数字信号处理》课程的一个核心知识点，谈及应用，我倒是能用的过来，在图像中简单地说就是掩模相乘，不过上次师兄谈及一个卷积优化方法，我想了想居然只想到行列分开这个。现在只能重新复习一下知识点。

学图像的应该都知道，卷积运算在图像处理领域应用相当广泛，例如在图像滤波、增强、分析等处理时都要用到卷积运算，它实质上是一种矩阵运算，其特点是运算量大，并且数据复用率高。

下面首先来回顾一下一维卷积：

卷积定义：





而在图像处理中，用得主要是离散二维卷积



OpenCV中的卷积函数

filter2D——对图像作卷积运算.

C++: void filter2D(InputArray src, OutputArray dst, int ddepth, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )

C: void cvFilter2D(const CvArr* src, CvArr* dst, const CvMat* kernel, CvPoint anchor=cvPoint(-1,-1) )

参数：

src – 输入图像. dst –与输入图像大小相同通道数相同的输出图像 . ddepth – desired depth of the destination image; if it is negative, it will be the same as src.depth(); the following combinations of src.depth() andddepth are supported: src.depth() = CV_8U, ddepth = -1/CV_16S/CV_32F/CV_64F src.depth() = CV_16U/CV_16S, ddepth = -1/CV_32F/CV_64F src.depth() = CV_32F, ddepth = -1/CV_32F/CV_64F src.depth() = CV_64F, ddepth = -1/CV_64F when ddepth=-1, the output image will have the same depth as the source. kernel – convolution kernel (or rather a correlation kernel), a single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes usingsplit() and process them individually. anchor – anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center. delta – optional value added to the filtered pixels before storing them in dst. borderType – pixel extrapolation method (seeborderInterpolate() for details)

Matlab中的卷积及例子

线性卷积与圆周卷积的区别