In digital image processing, histogram equalization is a common practice used to adjust brightness, contrast, and image sharpening. It involves statistical analysis of the histogram of different colors in an image, followed by remapping the colors based on this analysis to achieve the desired adjustments in contrast and sharpness.

1. Histogram of Digital Images

A histogram refers to the statistical analysis of all the pixel colors in an entire image, presenting the statistical data of different colors in the form of a histogram.

For example, consider the following two images:

Their corresponding histograms are:

From these figures, it can be observed that an image with high contrast has a wider distribution of colors, while an image with low contrast has colors concentrated in a specific region. In the second image, the pixel values are concentrated on a single color, resulting in image blurring.

Using image histograms, the color distribution of an image can be clearly understood, providing guidance on how to enhance images and improve contrast.

2. Histogram Equalization

Histogram equalization, as the name suggests, aims to equalize the color distribution in an image histogram. Specifically, it redistributes colors that were previously too concentrated in the histogram, spreading them out to occupy the appropriate pixel values. The specific method is as follows (using 3-bit 8-color as an example):

Consider a 64x64 pixel image (M=64, N=64, MN=4096) with 3 bits (L=8) representing colors, ranging from [0,L-1], i.e., [0,7]. By performing statistical analysis on the pixel values of the entire image, we obtain the following statistical data:

图像配准即对于内容大致相同，但在角度，大小或是其他几何位置上有所偏差的两张或几张图像进行坐标变换。使得这些图像矫正到同一规整的大小，角度，坐标上。

图像配准对于图像拼接，制作全景图像，进行环境识别等方面有很大的用处。其原理简单，使用矩阵运算转换速度也很快，在选取映射点合适的情况下，配准效果很好。具体的转化方法如下：

Image registration refers to the process of transforming the coordinates of two or more images that have similar content but differ in aspects like angle, size, or geometric position. This transformation aims to align these images to the same standardized size, angle, and coordinates.

Image registration finds extensive applications in image stitching, creating panoramic images, and environmental recognition. Its principle is simple, utilizing matrix operations for fast transformation. With appropriately selected mapping points, registration yields excellent results. The specific transformation method is outlined as follows:

Registration Algorithm

Taking image A as the reference for registering image B.

Selecting n pixels in image A: \[ p_1(x_1,y_1,1),p_2(x_2,y_2,1),...,p_n(x_n,y_n,1) \] Forms a coordinate matrix: \[ P=\begin{bmatrix}x_1 &x_2 &...&x_n\\y_1&y_2&...&y_n\\1&1&...&1\end{bmatrix} \] Selecting n pixels corresponding to these n points in image B: \[ q_1(x_1,y_1),q_2(x_2,y_2),...,q_n(x_n,y_n) \] Forms another coordinate matrix: \[ Q=\begin{bmatrix}x_1 &x_2 &...&x_n\\y_1&y_2&...&y_n\\1&1&...&1\end{bmatrix} \]

If image B can be translated, rotated, and scaled to match image A, then there exists a transformation matrix H such that: \[ Q=HP \] Thus, if the transformation matrix H can be computed, we can use the following formula: \[ H^{-1}Q=P \] Mapping each pixel in image B to the corresponding position in image A, thereby obtaining image B registered with image A as the template.

Given the point sets P and Q already selected in images A and B, it's evident that \[ H=QP^{-1} \]

As P is not a square matrix, it cannot be inverted directly. Here, we find the pseudo-inverse.

At this point, we've outlined the basic process of image registration:

• Select corresponding point sets $ P $ and $ Q $ in the template image and the image to be registered.
• Compute the transformation matrix $ H $ using the formula $ H=QP^{-1} $.
• Map the image to be registered onto the template image using the transformation matrix.

Registration Example

For instance: using image A as the template to register image B:

Selecting seven points within them, distributed as follows:

Note: The coordinate system displayed by the drawing tool is opposite to the (x, y) coordinates. Pay attention when recording coordinates.

\[ P=\begin{bmatrix}1448&1694&1756&383&2290&2035&2150\\1308&1198&2744&2516&933&2693&1968\\1&1&1&1&1&1&1\end{bmatrix} \]

\[ Q=\begin{bmatrix}1042&1252&1708&323&1761&1966&1890\\1077&907&2387&2519&498&2265&1535\\1&1&1&1&1&1&1\end{bmatrix} \]

The computed transformation matrix: \[ H=QP^{-1}=\begin{bmatrix}0.9668&0.2565&693.0275\\-0.2570&0.9671&184.1373\\0.0000&0.0000&1.0000\end{bmatrix} \] Transforming image B using the transformation matrix yields the following result:

Source Code:

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import cv2 as cv
import numpy as np
import math

imgA=cv.imread("Image A.jpg?x-oss-process=style/webp")
imgB=cv.imread("Image B.jpg?x-oss-process=style/webp")
point_A=np.array([[1448,1694,1756,383,2290,2035,2150],[1308,1198,2744,2516,933,2693,1968],[1,1,1,1,1,1,1]])
point_B=np.array([[1042,1252,1708,323,1761,1966,1890],[1077,907,2387,2519,498,2265,1535],[1,1,1,1,1,1,1]])
H=np.dot(point_B,np.linalg.pinv(point_A))
print(H)
shape_A=imgA.shape
shape_B=imgB.shape
result,mistake=np.zeros(shape_A),np.zeros(shape_A)
result=result.astype(np.uint8)
mistake=mistake.astype(np.uint8)
for i in range(shape_A[0]):
    for j in range(shape_A[1]):
        address=np.dot(H,np.array([i,j,1]))
        address=address.astype(np.int)
        if address[0]>0 and address[0]<shape_B[0] and address[1]>0 and address[1]<shape_B[1]:
            result[i][j]=imgB[address[0]][address[1]]
            mistake[i][j]=imgA[i][j]-result[i][j]
cv.namedWindow("test",cv.WINDOW_NORMAL)
cv.imshow("test",mistake)
cv.waitKey(0)
cv.imwrite("result.jpg?x-oss-process=style/webp",result)
cv.imwrite("mistake.jpg?x-oss-process=style/webp",mistake)

Conclusion

The image registration algorithm is relatively straightforward. With well-selected coordinate points, the computed registration results are commendable. However, manual point selection was employed in this instance. For large-scale or real-time image registration, automated point selection becomes essential. Thus, devising a method for the program to select appropriate and corresponding point sets poses a challenge in image registration.

一些markdown之外的语法规则，让文章更美观

本文摘录自 周宇峰的博客 原文链接

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Some syntax rules beyond markdown make the article more aesthetically pleasing.

> This article is excerpted from [Zhou Yufeng's blog](https://yfzhou.coding.me/2018/08/27/Hexo-Next%E6%90%AD%E5%BB%BA%E4%B8%AA%E4%BA%BA%E5%8D%9A%E5%AE%A2%EF%BC%88%E4%B8%BB%E9%A2%98%E4%BC%98%E5%8C%96%EF%BC%89/).

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Effect:
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Life is like a mirror,
Knowing oneself from the mirror,
I call it a top priority,
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Life is like a mirror, Knowing oneself from the mirror, I call it a top priority, It is also the goal we pursue!

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For more tags provided by NexT theme, click: [http://theme-next.iissnan.com/tag-plugins.html](http://theme-next.iissnan.com/tag-plugins.html)

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<div class="note danger no-icon"><p>danger no-icon</p></div>

First, configure in the theme configuration file:

Note tag (bs-callout).

note: # Style style: flat # Whether to have icons icons: true # Rounded rectangle border_radius: 3 light_bg_offset:

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{% label default@default %}

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{% label danger@danger %}

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Effect:{% btn https://www.baidu.com, Click to download Baidu, download fa-lg fa-fw %}

数字图像在当代社会有着非常重要的作用，图像的插值作为一种基本的图像处理方法，在很多情况下可以很好的提高图像分辨率，提升图像观感。图像的插值方法主要有以下三种：

• 最近邻插值
• 双线性插值
• 双三次插值
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Digital images play a very important role in contemporary society. Image interpolation, as a basic image processing method, can effectively improve image resolution and enhance visual perception in many cases. There are three main interpolation methods for images:

• Nearest Neighbor Interpolation
• Bilinear Interpolation
• Bicubic Interpolation
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