OpenCV的图像滤波平滑操作

cv2.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) $\rightarrow$ dst

src – input image.
dst – output image of the same size and the same number of channels as src.
ddepth –
desired depth of the destination image; if it is negative, it will be the same as src.depth(); the followin
– 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 using split() 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 (see borderInterpolate() for details).

将一个图片与一个核做卷积

cv2.blur(src, ksize[, dst[, anchor[, borderType]]]) $\rightarrow$ dst

src – input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst – output image of the same size and type as src.
ksize – blurring kernel size.
anchor – anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType – border mode used to extrapolate pixels outside of the image.
The function smoothes an image using the kernel: $k = \frac{1}{ksize.width*ksize.height}*\begin{bmatrix} 1 &1 &... &1 \\ 1 &1 &... &1\\ &......... \\ 1 &1 &... &1 \end{bmatrix}$

cv2.boxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) $\rightarrow$ dst

src – input image.
dst – output image of the same size and type as src.
ddepth – the output image depth (-1 to use src.depth()).
ksize – blurring kernel size.
anchor – anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize – flag, specifying whether the kernel is normalized by its area or not.
borderType – border mode used to extrapolate pixels outside of the image.
The function smoothes an image using the kernel: $k = \alpha*\begin{bmatrix} 1 &1 &... &1 \\ 1 &1 &... &1\\ &......... \\ 1 &1 &... &1 \end{bmatrix}$
where
$\alpha = \left\{\begin{matrix} \frac{1}{ksize.width*ksize.height}&&when&normalize = True\\ 1&&otherwise \end{matrix}\right.$

标准的高斯核函数进行滤波。不考虑像素是否具有相同的灰度值，也不考虑像素是否位于边缘，结果则趋向于模糊边缘，有时不受欢迎的。
cv2.GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) $\rightarrow$ dst

src – input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst – output image of the same size and type as src.
ksize – Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero’s and then they are computed from sigma* .
sigmaX – Gaussian kernel standard deviation in X direction.
sigmaY – Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height , respectively (see getGaussianKernel() for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType – pixel extrapolation method (see borderInterpolate() for details).

*如果想要创建一个高斯核，你可以用cv2.getGaussianKernel( kszie , sigma [,ktype])函数.
在这里插入图片描述

中心元素的像素值可能是原始图像中不存在的值，而中指滤波器则是原图像的值，有效的减小了噪声，对于椒盐噪声。核大小必须是奇数值。
非线性滤波:Blurs an image using
cv2.medianBlur(src, ksize[, dst]) $\rightarrow$ dst

src – input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
dst – destination array of the same size and type as src.
ksize – aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 …

非线性滤波:是结合图像的空间邻近度和像素值相似度的一种折中处理，同时考虑空域信息和灰度相似性，达到保边去噪的目的。双边滤波器的好处是可以做边缘保存（edge preserving），一般过去用的维纳滤波或者高斯滤波去降噪，都会较明显地模糊边缘，对于高频细节的保护效果并不明显。

原理：通过一个颜色差范围和像素距离范围来确定权重，进而影响中心像素的像素值。对于空间域（spatial domain ）和像素范围域（range domain）来分别介绍。

which was defined for, and is highly effective at noise removal while preserving edges.But the operation is slower compared to other filters.

cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]]) $\rightarrow$ dst

扫描二维码关注公众号，回复： 4759929 查看本文章

src – Source 8-bit or floating-point, 1-channel or 3-channel image.
dst – Destination image of the same size and type as src .
d – Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
sigmaColor – Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.定义多大的颜色差来起作用。
sigmaSpace – Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace .定义多大的距离内的像素起作用。