What are the common methods of digital image processing?

What is digital image processing?

Digital Image Processing is a method and technique for removing noise, enhancing, restoring, segmenting, and extracting features from a computer by a computer. The generation and rapid development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, extensive agriculture, animal husbandry, forestry, and environment. Demand for applications in military, industrial and medical applications.

Digital Image Processing, also known as computer image processing, refers to the process of converting an image signal into a digital signal and processing it with a computer.

The main purpose of digital image processing

In general, the main purpose of processing (or processing, analyzing) images is threefold.

(1) Improve the visual quality of the image, such as brightness, color conversion, enhancement, suppression of certain components, geometric transformation of the image, etc., to improve the image quality.

(2) Extracting certain features or special information contained in the image, which are often convenient for computer analysis of images. The process of extracting features or information is preprocessing of pattern recognition or computer vision. The extracted features may include many aspects such as frequency domain features, grayscale or color features, boundary features, region features, texture features, shape features, topological features, and relationship structures.

(3) Transformation, encoding and compression of image data to facilitate image storage and transmission. Regardless of the purpose of image processing, an image processing system composed of a computer and an image-dedicated device is required to input, process, and output image data.

Common methods of digital image processing

The common methods of digital image processing have the following aspects:

1) Image transformation: Since the image array is large, processing directly in the spatial domain involves a large amount of computation. Therefore, various image transformation methods, such as Fourier transform, Walsh transform, discrete cosine transform, and other indirect processing techniques are often used to convert the processing of the spatial domain into the transform domain processing, which not only reduces the amount of calculation, but also obtains more effective Processing (such as Fourier transform can be digitally filtered in the frequency domain). The wavelet transform of the emerging research has good localization characteristics in both the time domain and the frequency domain. It also has a wide and effective application in image processing.

2) Image coding compression: Image coding compression technology can reduce the amount of data (ie, the number of bits) of the description image in order to save image transmission, processing time and reduce the memory capacity occupied. Compression can be obtained without distortion, or under permissible distortion conditions. Coding is the most important method in compression technology. It is the earliest and mature technology in image processing technology.

3) Image Enhancement and Restoration: The purpose of image enhancement and restoration is to improve the quality of the image, such as removing noise and improving the sharpness of the image. Image enhancement does not consider the cause of image degradation, highlighting the portion of interest in the image. For example, by strengthening the high-frequency components of the image, the contours of the objects in the image are clear and the details are obvious; for example, the enhancement of the low-frequency components can reduce the influence of noise in the image. Image restoration requires a certain understanding of the cause of image degradation. Generally speaking, a "degradation model" should be established according to the degradation process, and then some filtering method is used to restore or reconstruct the original image.

4) Image segmentation: Image segmentation is one of the key technologies in digital image processing. Image segmentation is the extraction of meaningful features from images. The meaningful features are edges, regions, etc. in the image, which is the basis for further image recognition, analysis and understanding. Although many methods of edge extraction and region segmentation have been studied, there is no effective method that is generally applicable to various images. Therefore, the research on image segmentation is still in-depth, and it is one of the research hotspots in image processing.

5) Image Description: Image description is a necessary prerequisite for image recognition and understanding. As the simplest binary image, its geometric characteristics can be used to describe the characteristics of the object. The general image description method uses two-dimensional shape description, which has two kinds of methods: boundary description and region description. Two-dimensional texture feature descriptions can be used for special texture images. With the in-depth development of image processing research, research on three-dimensional object description has begun, and methods such as volume description, surface description, and generalized cylinder description have been proposed.

6) Image classification (recognition): Image classification (recognition) belongs to the category of pattern recognition. The main content is that after some preprocessing (enhancement, restoration, compression), the image segmentation and feature extraction are performed to judge the classification. Image classification often uses classical pattern recognition methods, including statistical pattern classification and syntactic (structure) pattern classification. In recent years, the newly developed fuzzy pattern recognition and artificial neural network pattern classification have been paid more and more attention in image recognition.

Digital image processing application tool

Digital image processing tools can be divided into three main categories:

The first type includes various orthogonal transform and image filtering methods, which have the common point of transforming the image into other domains (such as the frequency domain) for processing (such as filtering), and then transforming into the original space (domain).

The second type of method is to process images directly in the spatial domain, which includes various statistical methods, differential methods, and other mathematical methods.

The third type is mathematical morphology operation, which is different from the commonly used methods of frequency domain and spatial domain. It is based on integral geometry and random set theory.

Since the amount of data of the processed image is very large and many operations are essentially parallel, the image parallel processing structure and the image parallel processing algorithm are also the main research directions in image processing.