Digital image processing analysis generally refers to digital image processing. A digital image is a large two-dimensional array obtained through sampling and digitization by devices such as digital cameras and scanners. The elements of this array are called pixels, and their values are integers known as grayscale values. The main contents of image processing technology include image compression, enhancement, and restoration, as well as matching, description, and recognition.

Common image processing techniques include image digitization, image coding, image enhancement, image restoration, image segmentation, and image analysis. While some of these processes can also be achieved through optical methods or analog techniques, they are far less flexible and convenient than digital image processing. Therefore, digital image processing has become the predominant approach in the field.

The various aspects of image processing are interconnected. A practical image processing system typically combines several image processing techniques to achieve the desired results. Applications such as segmentation, description, and recognition for image analysis and understanding are used in various automated detection systems, including character and graphic recognition, robotic assembly and inspection of products, automatic military target identification and tracking, fingerprint recognition, and the automatic processing of X-ray images and blood samples. In such applications, a comprehensive use of pattern recognition and computer vision technologies is often required.

In a sense, image processing analysis is a part of machine vision. In another sense, it is a broader discipline. However, in reality, these two fields are inseparable and mutually supportive.