In machine vision systems, visual information processing techniques generally rely on image processing methods, typically including data encoding, image enhancement, image transmission and data smoothing, edge sharpening, image segmentation, image feature extraction, image recognition and understanding, and related aspects. Through these image processing steps, the quality of the output image is significantly improved, not only enhancing the visual effect but also facilitating computer analysis, processing, and recognition. The following section from China Machine Vision Mall ( www.china-vision.com.cn ) will explain image processing techniques for machine vision systems.
Image enhancement in machine vision systems involves adjusting quality metrics such as contrast and saturation to highlight important details and improve overall image quality and visual appeal. Typically, grayscale histogram modification techniques are used for image enhancement, improving the image's realism and display quality from multiple perspectives. A grayscale histogram is a statistical chart representing the grayscale distribution of an image; its parameters are closely related to contrast and are a crucial indicator of image quality.
Generally, a two-dimensional digital image represented in a computer can be represented as a digital matrix. The elements in the matrix are the gray values of the image at corresponding coordinate positions. These gray values are discretized integers, typically taking values of 0, 1, ..., 255.
Some might wonder why it's a string of discrete numbers. This is because the value range represented by a single byte in a computer is typically between 0 and 255. Furthermore, the human eye can only distinguish about 32 gray levels. Therefore, using one byte to represent gray levels is sufficient and very suitable for representing image quality.
However, since histograms can only count the probability of a certain level of grayscale pixel occurrence, they cannot reflect image quality by showing the two-dimensional coordinates of that pixel in the image. Therefore, different images are very likely to have the same histogram. By observing the shape of the grayscale histogram, we can determine the image's sharpness and black-and-white contrast. This is the image processing technique in machine vision systems, and the grayscale histogram that reflects image quality.
