Methods/steps
1, image enhancement
Image enhancement is to improve the quality and clarity of the image.
It highlights the information in a certain part of the image according to specific requirements, while weakening or removing some unnecessary information processing methods. Its main purpose is to make the processed image more suitable for a certain application. Histogram correction, image smoothing, image sharpening and color processing are the current image enhancement methods.
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2. Image restoration
Image restoration and image enhancement have the same purpose, both of which are to improve image quality. The difference is that image enhancement is to improve the quality of the original image, while image restoration is to restore the image in the degraded image.
By eliminating or reducing image blur, image annoyance and noise, the original real image can be obtained as much as possible. In order to restore the image, we must first find out the causes of image degradation, analyze the factors that cause the degradation, establish the corresponding mathematical model, and restore the image by adaptive methods.
3. Image coding
Image coding and compression technology is to compress the image on the premise of ensuring the image quality. If the image data is not compressed, the processing speed of the computer will be affected. There will be many mismatches, resulting in contradictions that cannot be alleviated. If the image signal is compressed, it will be very beneficial to image transmission and storage. Under the condition of existing hardware facilities, compressing the image signal itself is the way to solve the contradiction and mismatch. Using compression technology can reduce the amount of image data, thus saving the time of image transmission and processing and reducing the occupied memory capacity.
4. Image recognition
Image recognition belongs to the category of pattern recognition, and its main content is image segmentation and feature extraction after certain preprocessing, so as to make decision classification. Statistical pattern classification and structural pattern classification are commonly used pattern recognition methods.
5. Image segmentation method
Image segmentation is one of the most critical technologies in image processing. The commonly used segmentation methods are region-based segmentation method and edge-based segmentation method respectively. As the name implies, the region-based segmentation method is to divide the image into several non-overlapping regions, and there is a certain similarity in each region, which makes the similarity in each region greater than the feature similarity between regions. Edge-based segmentation method is to detect the local discontinuity of the image first, and then connect the discontinuous parts into a boundary, which divides the image into different regions.
6. Image analysis
Using image segmentation method to extract useful information from the image to get some numerical results, thus establishing an objective description of the image. This description can not only answer whether there is a specific object in the image, but also describe the image content in detail.
7. Digitization of images
Through sampling and quantization, natural images are converted into digital forms suitable for computer processing. An image is represented as a digital matrix inside a computer, and each element in the matrix is called a pixel.