Method for improving latent image contour hiding performance
A latent image and contour technology, applied in the field of image processing, can solve the problems of large scale coefficient, narrow adaptation range, accumulated error and so on
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Embodiment 1
[0073] A method for improving the performance of latent image contour concealment, comprising the steps of:
[0074] step 1),
[0075] see figure 1 , input the target image (named "still life"), the target image includes x×y pixels, the subscript (x, y) represents the coordinates of the pixel, color-separate the CMYK channel of the target image, and the color separation is pair The CMYK channel of the target image is separated to obtain four 256-level grayscale images, so four channels of C, M, Y and K are obtained; the C channel is selected as the invisible channel;
[0076] Step (2),
[0077] Adopt amplitude modulation screening technology, set the generation method of screen dots as model method, screen processing to get the halftone image of 4 channels composed of screen dots; set screen screening parameters when digital screening, screen screening parameters include screen angle, screen line Number, dot shape and output resolution, the screening angle of the invisible ...
Embodiment 2
[0112] Compared with Embodiment 1, the difference between Embodiment 2 and Embodiment 1 is: different target images (named "clock") and hidden information are used, and the invisible channel is the M channel; the generation method of setting network points is the growth model method, and the growth model The method refers to compressing the number of dot models in the model method according to the ratio to obtain another basic model. When the gray value decreases or increases by one level, one more or one less recording point is exposed in the corresponding basic model, thus expressing extract all tones in the target image, combined with Figure 5-8 It can be seen that the hidden information in Example 2 is completely invisible in the synthesized target image to be output, has very good imperceptibility, and can be clearly extracted by the test grating during recognition.
Embodiment 3
[0114] Compared with Embodiment 1, Embodiment 3 differs in that it uses a different target image (named "cake") and hidden information. combine Figures 9 to 12 It can be seen that the hidden information in Example 3 is also completely invisible in the synthesized target image to be output, has very good imperceptibility, and can be extracted very clearly by the test grating during recognition.
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