I found an article about the concept of digital image processing that is relatively easy to understand. The core part is extracted as follows:
Basic properties of images
Brightness: Also known as grayscale, it is the change in light and shade of color, usually expressed from 0% to 100% (from black to white). The following three pictures are a comparison of different brightness.
The effect of brightness on image color
Contrast: It is the ratio of the black and white of the picture, that is, the gradient level from black to white. The larger the ratio, the more gradient levels from black to white, and the richer the color expression.
The effect of contrast on image color performance
Histogram: Indicates the number of pixels with each gray level in the image, reflecting the frequency of each gray level in the image. The storage form of the image in the computer is like a matrix composed of many points. These points are neatly arranged in rows and columns. The value on each point is the gray value of the image, and the histogram is the gray value of each gray in this point matrix. the number of occurrences. We can specifically look at the grayscale histograms of the following two different graphs:
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Histogram equalization
Converting an image into another image with a balanced histogram through grayscale transformation, that is, the process of having the same number of pixels in a certain grayscale range. The following is the graph change before and after histogram equalization and the histogram change:
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Addition and subtraction of images
Addition and subtraction of two images: Adding and subtracting images is to add and subtract the grayscale values on the storage rectangle point column corresponding to the image. Image addition can add the content of one image to another image, can achieve double exposure, and can also average multiple images of the same scene, which can reduce noise. Image subtraction can be used for motion detection or to remove unwanted additive patterns in images.
Image addition example: The operation in the figure is: (a)+(b)=(c)
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Example of image subtraction operation: the operation in the figure is (a)-(b)=(c)
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image noise
Image noise: Just like hearing, when the other party is talking on the phone, we sometimes hear so loud noise that it is difficult to hear what the other party is saying. Similarly, for images, we can see an image very clearly, but sometimes there are some patterns we don't need on the image, so that we can't see a picture clearly, which is the noise of the image.
Commonly used image denoising methods
Common denoising methods: mainly use filters to filter noisy images.
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| figure with noise |
Arithmetic mean filtered graph |
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| Median filtered image |
noise free map |