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Scanning transmission image restoration method and device and storage medium

A transmission image and recovery method technology, applied in the field of image processing, can solve the problems of difficulty in data set acquisition, difficulty in effectively removing scanning transmission image information, difficulty in generalization, etc., to achieve good separation effect, high generalization ability, and improved performance force effect

Pending Publication Date: 2022-05-20
YANTAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] 3. The method based on training and learning requires a large number of paired data sets during the training process. The data set collection is difficult and there is over-fitting phenomenon, which is difficult to generalize
[0007] Based on the above problems, it is still difficult for existing scanners on the market to effectively remove the scanned transmission image information.

Method used

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  • Scanning transmission image restoration method and device and storage medium
  • Scanning transmission image restoration method and device and storage medium
  • Scanning transmission image restoration method and device and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] In view of the problem that the text content on the back side will be transmitted to the front page during the scanning process of thinner paper printed matter or written matter (for example: ordinary paper books, teaching materials, test papers), thereby affecting the normal reading of readers, the embodiment of the present invention provides A scanning transmission image restoration method is proposed, see figure 1 , the method includes the following steps:

[0058] 101: Construct a preset network composed of a generator network and a discriminator network;

[0059] Wherein, the preset network includes: a generator network and a discriminator network, and the generator network includes: a first generator G d and the second generator G h ; The discriminator network includes: the first discriminator D d and the second discriminator D h , set the generator network and the discriminator network to the confrontational learning mode, the generator network and the discri...

Embodiment 2

[0094] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas, accompanying drawings, and examples, see the following description for details:

[0095] 201: The problem of scanning transmission image restoration can be regarded as a conversion problem from image to image, namely: the problem of converting a scanning transmission image to a non-scanning transmission image;

[0096] In the process of training the model, a large number of scanned transmission image data sets are required as the data basis. However, there is no directly usable scanned transmission image dataset in reality. Therefore, the first step is to construct a dataset of scanned transmission images. In order to ensure good generalization performance of subsequent models, the types and styles of scanned transmission images should be as many as possible. Collect different styles of text materials and make scanning transmission images. In order to ensure the g...

Embodiment 3

[0117]Below in conjunction with concrete evaluation standard, accompanying drawing and table, the scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0118] In order to objectively evaluate the restoration effect of the transmission image, the peak signal-to-noise ratio (PSNR, Peak Signal-to-NoiseRatio) and structural similarity (SSIM, Structural SIMilarity) are used as the evaluation criteria for the restored image quality, and the given size is m×n pixels Image I and image K, PSNR and SSIM are defined as follows:

[0119] (1)

[0120] in, is the mean square error, I(i, j) and K(i, j) are the pixel values ​​of image I and image K at position (i, j) respectively.

[0121] (2)

[0122] Among them, μ I is the mean value of I, μ K is the mean of K, is the variance of I, is the variance of K, σ IK is the covariance of I and K, c 1 =(k 1 L) 2 , c 2 =(k 2 L) 2 It is two constants to avoid dividing by...

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Abstract

The invention discloses a scanning transmission image restoration method and device and a storage medium, and relates to the field of image processing, the method mainly utilizes a cyclic consistency generative adversarial network in the field of deep learning to restore a scanning image, considers the complexity of text distribution in an actual scanning transmission image, combines human visual features, and improves the image restoration efficiency. A channel attention mechanism and a region attention mechanism are integrated in the network, different features and different regions are processed non-uniformly, constraint transfer learning and a loop structure of the loop consistency generation adversarial network are utilized, and finally an unsupervised scanning transmission image recovery task of non-paired images is completed. The method has good image recovery performance, and has good robustness and adaptability to scanning transmission image data under various conditions.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a scanning transmission image restoration method, device and storage medium. Background technique [0002] Scanners are the main way to solve the digitization of various documents, photos, slides, negatives and other manuscripts since the 1980s. widely used in. With the development of network and multimedia and higher standards for image quality of digital documents, for overlapping scanning using the transmissive scanning principle, the content on the back will usually be transmitted to the content on the front, thus affecting normal reading and use. [0003] Currently, the problem with scanned transmission image restoration is: [0004] 1. The traditional method is to restore the image by detecting the peak value of the gray histogram of the scanned image. The non-unique threshold setting makes the restoration process difficult to achieve and the result is not ideal; [0005]...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06K9/62G06V10/774G06V10/764G06V10/80G06V10/82G06N3/04G06N3/08H04N1/04
CPCG06N3/084H04N1/04G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/20221G06N3/045G06F18/241G06F18/253G06F18/214G06T5/00
Inventor 倪梦莹马咏莉徐金东
Owner YANTAI UNIV