[数字图像处理]简单的几何学图像变换与图像配准

时间:2023-02-07 13:48:16

1.图像的几何学变换

       之前的博文里,我简单的介绍了图像的放大与缩小。放大与缩小也算是图像的几何学变换,本文介绍了其他的几何学变换,包括旋转、水平倾斜和垂直倾斜(当然,还有水平移动与垂直移动。这些变换很简单,不需要插值,所以这里就不着重介绍了)。

       假设输入图像为G(u,v),其变换后的图像为F(x,y)。其变化的方法,如下所示。

[数字图像处理]简单的几何学图像变换与图像配准

       图像的几何学变换,主要有两种向前映射与向后映射。

       1.1向前映射

       所谓向前映射,就是从输入图像为g(u,v)的(0,0)点开始,将g(u,v)遍历一遍,依次计算g(u,v)变换后的坐标。当然,计算出来的坐标不会是整数(很大程度上不会是整数),就像下图所示那样。 [数字图像处理]简单的几何学图像变换与图像配准
      借由上图,我们重新理解一些向前映射。假设图像的一部分[数字图像处理]简单的几何学图像变换与图像配准这5个点,经过变换,我们得到了右边[数字图像处理]简单的几何学图像变换与图像配准的5个点。其实,这个5个点经过变换之后,计算出来的坐标并不是整数。假设现在遍历到[数字图像处理]简单的几何学图像变换与图像配准这个点(图中以蓝色表示),通过计算得到的点是[数字图像处理]简单的几何学图像变换与图像配准。我们将[数字图像处理]简单的几何学图像变换与图像配准的坐标进行四舍五入,将[数字图像处理]简单的几何学图像变换与图像配准的值赋值给[数字图像处理]简单的几何学图像变换与图像配准(这种处理相当于选择最近的点,属于最初级的插值方法)。        使用上述的向前映射的思想,将图像旋转,我们能得到如下效果。 [数字图像处理]简单的几何学图像变换与图像配准
       可以看出来,所得到的结果非常“斑驳”。其原因是,我们将g(u,v)进行变换,计算之后,所得到的坐标四舍五入之后,有的点没有被赋值,而有点被赋值了多次。这就产生了“空穴”,所以让变换后的图像非常斑驳。        我看了一些文章,基本到这里都会说,“由于向前映射会产生空穴,所以向前映射一般不使用”。诸如此类的话,其实这样理解不太对,产生空穴的根本原因是由于插值法的不恰当所产生的。使用向前映射,也可以不产生“空穴”,所使用的方法如下所示。 [数字图像处理]简单的几何学图像变换与图像配准
       我们可以通过最接近[数字图像处理]简单的几何学图像变换与图像配准的四个点,去确定[数字图像处理]简单的几何学图像变换与图像配准的值。这样的话,就可以不产生空穴。但是,这个方法实现起来很困难,所以,向前映射才一般不被使用。

       1.2 向后映射

       向后映射,就是将输出图像f(x,y)遍历一遍,然后计算输出(x,y)的时候,所需要g(x,y)的坐标。当然,这个数不一定是整数。为了方便理解,还是看下图。 [数字图像处理]简单的几何学图像变换与图像配准
       假设,我们遍历到[数字图像处理]简单的几何学图像变换与图像配准,通过计算,我们得到[数字图像处理]简单的几何学图像变换与图像配准这样一个点。也就意味着,如果需要确定[数字图像处理]简单的几何学图像变换与图像配准的灰度值,那么,我们需要[数字图像处理]简单的几何学图像变换与图像配准点处的灰度值。这样,几何变换的问题又归结到了插值问题。最方便的办法就是选择最近的点,入上图的例子,[数字图像处理]简单的几何学图像变换与图像配准的灰度值等于[数字图像处理]简单的几何学图像变换与图像配准的灰度值。当然,还有更加“高级”点的方法,精度也还算可以。[数字图像处理]简单的几何学图像变换与图像配准的灰度值的确定方法如下所示。 [数字图像处理]简单的几何学图像变换与图像配准
       如上图,我们使用四个点([数字图像处理]简单的几何学图像变换与图像配准),去确定[数字图像处理]简单的几何学图像变换与图像配准的值。首先[数字图像处理]简单的几何学图像变换与图像配准[数字图像处理]简单的几何学图像变换与图像配准之间使用线性插值,确定出[数字图像处理]简单的几何学图像变换与图像配准的值。同样的方法,确定出[数字图像处理]简单的几何学图像变换与图像配准的值。[数字图像处理]简单的几何学图像变换与图像配准[数字图像处理]简单的几何学图像变换与图像配准之间,进行线性插值,就可以得到[数字图像处理]简单的几何学图像变换与图像配准的值了。(图画的非常清楚了)        使用向后映射,将图像旋转,我们可以得到如下结果。 [数字图像处理]简单的几何学图像变换与图像配准

       1.3 上述两部分的Matlab代码

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close all;clear all;clc;
%% -----------Geometric_spatial_transform------------------f = imread('./letter_T.tif');f = mat2gray(f,[0 255]);
[M,N] = size(f);
%-----by forward mapping-----%g_fm = zeros(M,N);seta = -pi/8;
for v = (-M/2):1:(M/2)-1 for w = (-N/2):1:(N/2)-1 x = round(v*cos(seta) - w*sin(seta)); y = round(v*sin(seta) + w*cos(seta)); if (((y>=(-N/2))&&(y<=(N/2)))&&((x>=(-M/2))&&(x<=(M/2)))) g_fm(x+(M/2)+1,y+(N/2)+1) = f(v+(M/2)+1,w+(N/2)+1); end endend
figure();subplot(1,2,1);imshow(f,[0 1]);xlabel('a).Original Image');
subplot(1,2,2);imshow(g_fm,[0 1]);xlabel('b).Ruselt of Geometric spatial transform');
%-----by inverse mapping----g_im = zeros(M,N);seta = -pi/8;
for x = (-M/2):1:(M/2)-1 for y = (-N/2):1:(N/2)-1 v = x*cos(-seta) - y*sin(-seta); w = x*sin(-seta) + y*cos(-seta); if (((w>=(-N/2)+1)&&(w<=(N/2)-1))&&((v>=(-M/2)+1)&&(v<=(M/2)-1))) %g_im(x+(M/2)+1,y+(N/2)+1) = 1;%f(round(v+(M/2)+1),round(w+(N/2)+1)); Q_11 = f(floor(v)+(M/2)+1,floor(w)+(N/2)+1); Q_21 = f(floor(v)+(M/2)+1,ceil(w)+(N/2)+1); Q_12 = f(ceil(v)+(M/2)+1,floor(w)+(N/2)+1); Q_22 = f(ceil(v)+(M/2)+1,ceil(w)+(N/2)+1); R1 = (Q_21 - Q_11)*(w-floor(w)) + Q_11; R2 = (Q_22 - Q_12)*(w-floor(w)) + Q_12;
g_im(x+(M/2)+1,y+(N/2)+1) = (R2-R1)*(v-floor(v)) + R1; end endend
figure();subplot(1,2,1);imshow(f,[0 1]);xlabel('a).Original Image');
subplot(1,2,2);imshow(g_im,[0 1]);xlabel('b).Ruselt of Geometric spatial transform');
 来自CODE的代码片Geometric_spatial_transform.m

      2.图像的配准

      图像的配准,常常用于超解析领域。当然了,作为基础学习,博主没学那么深。这次的图像配准,试图去还原被倾斜变换的图像。首先,先将图像进行两个方向的倾斜。 [数字图像处理]简单的几何学图像变换与图像配准
       我们这次的目的是,将图像b).还原为a).。这次变换相对简单,我们使用如下模型去拟合变换关系。 [数字图像处理]简单的几何学图像变换与图像配准
我们在原图与变换后的图像上,选择出四个标准点,然后带入方程,并求出系数[数字图像处理]简单的几何学图像变换与图像配准。使用这个方程,将图像b).还原为a)。这个过程比较简单,下面是结果。另外,所选择的标准点,我已经在上图中用红圈标定出来了。 [数字图像处理]简单的几何学图像变换与图像配准
       可以看到,还原的效果非常的好(额,其实这也是应为畸变比较简单的缘故)。为了看出于原图的区别,我们做出了差分图像。可以看出来,还原不是完美的。        至此,上个代码作为本文的结尾。额,由于特征点的选择是手工的,这个代码可能没有多少意义。(不要吐槽最后这一句话。一般的,在使用特征点做图象配准的时候,一般选择在某个实际的物体上放入某个实际的标志,然后通过检测这个实际的标志,去进行变换。【这里可以参考《Digital Image Processing》 Rafael C. Gonzalez / Richard E. Woods的第二章的相关内容,这里有叙述的!!】)
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close all;clear all;clc;%% --------------image registration---------------------------f_Original = imread('./characters_test_pattern.tif');f_Original = mat2gray(f_Original,[0 255]);[M,N] = size(f_Original);
f = zeros(M+4,N+4);for x = 1:M f(x+2,:) = [0 0 f_Original(x,:) 0 0];end
[M,N] = size(f);
s_v = 0.3;s_w = 0.02;
P = round(M+s_v*N);Q = round(N+s_w*M);g_1 = zeros(P,N);
for x = (-P/2):1:(P/2)-1 for y = (-N/2):1:(N/2)-1 v = x - s_v * y; w = y; if (((w>=(-N/2))&&(w<=(N/2)-1))&&((v>=(-M/2))&&(v<=(M/2)-1))) Q_11 = f(floor(v)+(M/2)+1,floor(w)+(N/2)+1); Q_21 = f(floor(v)+(M/2)+1,ceil(w)+(N/2)+1); Q_12 = f(ceil(v)+(M/2)+1,floor(w)+(N/2)+1); Q_22 = f(ceil(v)+(M/2)+1,ceil(w)+(N/2)+1); R1 = (Q_21 - Q_11)*(w-floor(w)) + Q_11; R2 = (Q_22 - Q_12)*(w-floor(w)) + Q_12;
g_1(x+(P/2)+1,y+(N/2)+1) = (R2-R1)*(v-floor(v)) + R1; end endend
g = zeros(P,Q);
for x = (-P/2):1:(P/2)-1 for y = (-Q/2):1:(Q/2)-1 v = x; w = y - s_w * x; if (((w>=(-N/2))&&(w<=(N/2)-1))&&((v>=(-P/2))&&(v<=(P/2)-1))) Q_11 = g_1(floor(v)+(P/2)+1,floor(w)+(N/2)+1); Q_21 = g_1(floor(v)+(P/2)+1,ceil(w)+(N/2)+1); Q_12 = g_1(ceil(v)+(P/2)+1,floor(w)+(N/2)+1); Q_22 = g_1(ceil(v)+(P/2)+1,ceil(w)+(N/2)+1); R1 = (Q_21 - Q_11)*(w-floor(w)) + Q_11; R2 = (Q_22 - Q_12)*(w-floor(w)) + Q_12;
g(x+(P/2)+1,y+(Q/2)+1) = (R2-R1)*(v-floor(v)) + R1; end endend
figure();subplot(1,2,1);imshow(f,[0 1]);xlabel('a).Original Image');hold on;plot(621, 78,'ro','MarkerSize',7);plot(116,113,'ro','MarkerSize',7);plot( 85,649,'ro','MarkerSize',7);plot(624,641,'ro','MarkerSize',7);
subplot(1,2,2);imshow(g,[0 1]);xlabel('b).Ruselt of Geometric spatial transform');hold on;plot(625,264,'ro','MarkerSize',7);plot(116,147,'ro','MarkerSize',7);plot( 96,674,'ro','MarkerSize',7);plot(638,828,'ro','MarkerSize',7);%% f_reg = zeros(M,N);
Original = [621-(N/2) 78-(M/2) (621-(N/2))*(78-(M/2)) 1; 116-(N/2) 113-(M/2) (116-(N/2))*(113-(M/2)) 1; 85-(N/2) 649-(M/2) (85-(N/2))*(649-(M/2)) 1; 624-(N/2) 641-(M/2) (624-(N/2))*(641-(M/2)) 1]; Ruselt = [625-(Q/2);116-(Q/2);96-(Q/2);638-(Q/2)];
c = (Original)\Ruselt; %C1~C4
Ruselt = [264-(P/2);147-(P/2);674-(P/2);828-(P/2)];
c = [c (Original)\Ruselt]; %C5~C8 wwwwww
for y = (-M/2):1:(M/2)-1 for x = (-N/2):1:(N/2)-1 w = c(1,1)*y + c(2,1)*x + c(3,1)*x*y + c(4,1); v = c(1,2)*y + c(2,2)*x + c(3,2)*x*y + c(4,2); if (((w>=(-Q/2))&&(w<=(Q/2)-1))&&((v>=(-P/2))&&(v<=(P/2)-1))) Q_11 = g(floor(v)+(P/2)+1,floor(w)+(Q/2)+1); Q_21 = g(floor(v)+(P/2)+1,ceil(w)+(Q/2)+1); Q_12 = g(ceil(v)+(P/2)+1,floor(w)+(Q/2)+1); Q_22 = g(ceil(v)+(P/2)+1,ceil(w)+(Q/2)+1); R1 = (Q_21 - Q_11)*(w-floor(w)) + Q_11; R2 = (Q_22 - Q_12)*(w-floor(w)) + Q_12; f_reg(x+(N/2)+1,y+(M/2)+1) = (R2-R1)*(v-floor(v)) + R1; %g(round(v+(P/2)+1),round(w+(Q/2)+1)) = 0.5; end endend
g_diff = abs(f - f_reg);
figure();subplot(1,2,1);imshow(f_reg,[0 1]);xlabel('c).Ruselt of image registration');
subplot(1,2,2);imshow(g_diff,[0 1]);xlabel('d).Difference image');
 来自CODE的代码片image_registration.m
      原文发于博客:http://blog.csdn.net/thnh169/