Method for improving latent image boundary based on edge detection error diffusion algorithm

An error diffusion and edge detection technology, applied in the field of image processing, can solve problems such as the inability to extract complete graphic information, contour distortion, large scale coefficient, etc.

Inactive Publication Date: 2018-11-30
HEFEI XINYADA INTELLIGENT TECH CO LTD
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  • Abstract
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Problems solved by technology

In the improvement of the dot percentage, combined with the visual masking effect, the adaptation coefficient is set to 0.25. Although this uniform adaptive change of all edge dots can improve the concealment to a certain extent, the scope of adaptation is narrow and can only be used in Better results on images with flatter tones
[0005] Xie Shiqi from Xi'an University of Technology pointed out in "Study on Screening Error Analysis Based on Analog Printing Concept" that the traditional error diffusion method is to process error diffusion from the perspective of transmission error, and the proportional coefficients in each direction are fixed. The scale factor in the vertical direction is always larger than in other directions, which in some cases can cause cumulative errors, resulting in contour distortion
Based on the above, some studies have made further improvements, referring to the error diffusion method to improve the dot percentage on the boundary. Although the adaptive scale coefficients are different, their scale coefficients in the horizontal and vertical directions are always larger than those i

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  • Method for improving latent image boundary based on edge detection error diffusion algorithm
  • Method for improving latent image boundary based on edge detection error diffusion algorithm
  • Method for improving latent image boundary based on edge detection error diffusion algorithm

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Embodiment 1

[0071] A method for improving the boundary of a latent image based on an edge detection error diffusion algorithm, comprising the following steps:

[0072] step 1),

[0073] 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;

[0074] Step (2),

[0075] Adopt amplitude modulation screening technology, set the generation method of network dots as model method, and obtain the halftone image of 4 channels composed of network dots through the screening process; set the screening parameters when digital screening, the screening parameters include screening angle, screening line Number, dot shape and...

Embodiment 2

[0110] 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

[0112] 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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Abstract

The invention discloses a method for improving a latent image boundary based on an edge detection error diffusion algorithm, comprising the following steps: step (1), inputting a target image for color separation; step (2), performing amplitude screening; step (3), obtaining a latent image of hidden information; and step (4), adjusting dots at the boundary of and inside the latent image in a halftone picture of an invisible channel, including a primary adjustment and a secondary adjustment; and synthesizing to obtain a halftone picture of the target image embedded with the hidden information.The method adopts the difference dynamics of the dots of the boundary of the latent image to obtain an error dispersion coefficient, quantizes the error of the gray value according to the coefficientand performs dispersion on the surrounding dots, and associates the contour feature of the hidden information with the dispersion of the error, which may effectively improve the problem of anti-counterfeiting failure caused by that the outline of the hidden information reveals.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a latent image boundary improvement method based on an edge detection error diffusion algorithm. Background technique [0002] The grating anti-counterfeiting technology based on the Moiré effect is an important branch and research direction in the field of information hiding technology research. It embeds some anti-counterfeiting information directly into the digital carrier without affecting the use value of the original carrier, and it is not easy to be detected and modified again. But by using the detection grating, the anti-counterfeit information can be recognized and recognized by the user. The purpose of anti-counterfeiting is achieved by hiding the anti-counterfeiting information (hidden information) in the digital carrier (target image). [0003] However, in the anti-counterfeiting information of the prior art, the problem that often occurs in the ...

Claims

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Application Information

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IPC IPC(8): G06T1/00
CPCG06T1/0021G06T2201/0065
Inventor 张玉
Owner HEFEI XINYADA INTELLIGENT TECH CO LTD
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