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2022-08-09
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Image and image composition

no matter what form the image information input system of the electronic color separation machine adopts, it uses the light source to convert the image information of the original into optical signals, and then the optical signals are appropriately transformed and transmitted to the image processing system in the form of electrical signals or digital signals. From the perspective of image processing, the electronic color separation machine uses the grid image processing method and photoelectric conversion technology to decompose images and obtain image information, Therefore, before discussing the image input system of electronic color separation machine, it is necessary to introduce the relevant concepts

an image and its representation

in daily life, when we observe a scene from a certain point, the light emitted by the object (the radiation of the luminous object or the light reflected or transmitted by the object after being irradiated by the light source) enters the human eye and forms an image on the visual film of the human eye. This is the objective world seen by the human eye, which we call a scene. This "image" reflects the change of the brightness and color of the objective scene with the spatial position and direction, so "image" is a function of spatial coordinates. Optic film imaging is a natural physiological phenomenon, 2 It turns the screw on the zigzag rod, and it is only when human civilization has developed to a certain period that it is aware of its existence, and tries to record it by various means. All kinds of "images" recorded are called images. Therefore, image is the most important means for human beings to express and transmit information. Modern images include both images in the visible light range and images in the invisible light range that can be converted into images visible to the adult eye with the help of appropriate conversion devices

in a spatial image information, the intensity of light is its basic element, which varies with the spatial coordinates (x, y, z) of the image and the wavelength of light λ It changes with the change of time t, so the spatial image function can be expressed as:

for a planar image, it is expressed as:

If only the energy of light is considered without considering its wavelength, the image visually appears as a black-and-white (gray) image, which is called a black-and-white image or a monochromatic image, and its image function is:

where: vs( λ) Is the relative visual acuity function

when considering the color effect of light with different wavelengths, the image is visually represented as a color image, and its image function is

, in which:

is the primary color visual sensitivity function of red, green and blue in turn

the image whose content changes with time is called a moving image, and its image function is shown in the formula, while the image whose content does not change with time is called a still image, and the still image is the focus of this book, The image function is

because the printed images are still images, the color processing is also carried out after decomposition into three primary colors, and each color can be regarded as a monochrome image (black-and-white image), so the static black-and-white image is used as a model in the following study of Luo Xiang

is it overshoot? The spatial range of the image is infinite and the visual field of the human eye is limited. Therefore, for the convenience of research, we define the image in the bounded space that can be detected by vision, that is:

where LX, ly are the brightness limits of vision on X, y

to sum up, the value of the image function at a certain point is defined as light intensity or gray scale, which corresponds to the brightness of the image at this point and can be expressed by a positive real number, and the size of this value is limited, and a large image gray scale value means a large brightness value, on the contrary, a small image gray scale value means a small brightness value, that is:

where: BM - the maximum brightness value

therefore, the image function f (x, y) is a binary, bounded, Nonnegative continuous function. For reflection type images, the image function is:

where: II - incident brightness IO - reflected brightness

in color copied images, the layout is composed of images and words. Except for words and symbols that can be discharged by font, the layout part can be called image. Moreover, the pixels in the image represent the properties of the image. Let P (I, J), 0 ≤ I ≤ m, 0 ≤ J ≤ n be a pixel in the image, then (1) if P (I, J) {0, 1}, 0 ≤ I ≤ m, 0 ≤ J ≤ n, that is, the image has only two values, that is, the difference between foreground and background. This image is called a line graph in the printing industry and is called a binary image in image processing; (2) If the image p (I, J) indicates that the image has a certain brightness change, this kind of image is called continuous tone image in the printing industry, and it is called intensity (gray) image in image processing, in which it indicates that the image has not only brightness change, but also color change. Such images are called color images, and their representation methods include RGB system, ym-cbk system, luv system, etc

two digital images

images are divided into continuous images and discrete images. The so-called continuous image refers to the image with continuously changing f (x, y) and gray value I in the two-dimensional coordinate system. A typical representative of a continuous image is the image obtained by an optical lens system, such as a person, an aerial camera, etc. it has no unnatural feeling when observed with the eyes, so it is also called a simulated image. As shown in Figure 2.3, a discrete image takes t as the period, divides the X and Y coordinate axes into checkerboard grids, and takes only the gray values at the discrete intersection points. The image formed in this way is called a discrete image or a sampling image Therefore, when the discrete gray value is used to represent the gray value of an image, this image becomes called quantizing image The so-called digital image refers to the decomposition of the image into small discrete points called picture elements (pixels) as shown in Figure 2.4 with the continuous growth of the demand for global air transport and general aviation services, and the gray value of each pixel is represented by the quantized discrete value, that is, the integer value

the method of image decomposition into pixels is shown in Figure 2.5. According to the plane, there are square array, hexagonal array and triangular array, of which square array is the most commonly used

for an image, a triangular array is obtained from an analog image, of which the square array is the most commonly used. For an image, to obtain a digital image from an analog image, spatial sampling and quantization must be carried out according to the process shown in Figure 2.6

1. Spatial sampling

(1) the concept of spatial sampling

sampling refers to an operation that transforms spatially or temporally continuous images (analog images) into a set of discrete sampling points (pixels)

the image processed before printing basically adopts the distribution mode of two-dimensional plane information. To input these image information into the computer for processing, we must first transform the anti two-dimensional image signal into one-dimensional image signal, which must be realized by scanning. The most commonly used method is to scan along the horizontal or vertical direction in a straight line at a certain interval from top to bottom on the two-dimensional plane, so as to obtain the image gray value array, that is, a group of one-dimensional signals, and then calculate the value of each specific interval to obtain the discrete signal

suppose an image, if the number of pixels in the X direction is m and the number of pixels in the Y direction is n when sampling, the image uses discrete M × Represented by N pixels, that is, when processing this image, only m × Image grayscale of n points

in the actual sampling process, the selection of sampling point interval is an extremely critical problem. Since the image contains various degrees of subtle density changes, the part of the sampling point needs to be determined according to the degree of faithful reflection of the image. Sampling Theorm/Shannon Theorm points out that if the spatial frequency of a one-dimensional signal G (T) is limited below  then according to the formula, the sampling value g (it) sampled by t ≤ 1/2  interval is used, where i=..., -2, -1, 0, 1, 2..., G (t) can be truly restored (reconstructed)

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