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A Text Image Super-resolution Reconstruction Method Based on Conditional Generative Adversarial Network

A super-resolution reconstruction, text image technology, applied in image analysis, biological neural network model, graphics and image conversion, etc. Super-resolution reconstruction quality, avoid destroying effects

Active Publication Date: 2021-11-16
NANJING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

On the other hand, due to the influence of many factors such as the resolution of the image acquisition equipment in the natural scene, the intensity of the scene illumination, and the distance of the text, the resolution of the actually obtained text image is relatively low in many cases. processing such as identification poses considerable difficulties

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  • A Text Image Super-resolution Reconstruction Method Based on Conditional Generative Adversarial Network
  • A Text Image Super-resolution Reconstruction Method Based on Conditional Generative Adversarial Network
  • A Text Image Super-resolution Reconstruction Method Based on Conditional Generative Adversarial Network

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Embodiment Construction

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0044] A text image super-resolution reconstruction method based on conditional generative confrontation network, such as figure 1 shown, including the following steps:

[0045] (1) Construct a training image sample data set, including the following sub-steps:

[0046] (1.1) Carry out adaptive threshold segmentation on the high-resolution text image for training, and generate a text-non-text binary segmentation image of the same size as the origin...

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Abstract

The invention discloses a text image super-resolution reconstruction method based on a conditional generation confrontation network. This method modifies the structure of the common conditional generative adversarial network to make it more suitable for super-resolution reconstruction tasks of text images, and introduces and utilizes text-non-text binary segmentation images as additional training supervision for super-resolution reconstruction models. information, and combine the text-non-text binary segmentation information to construct the loss function of the model to constrain the training of the model, so that the super-resolution reconstruction model is more concentrated on the text part in the image. Compared with general image super-resolution methods, the text image super-resolution reconstruction method disclosed in the present invention utilizes the information of the text itself more fully and pertinently, and effectively improves the quality of text image super-resolution reconstruction.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a text image super-resolution reconstruction method. Background technique [0002] With the increasingly widespread use of various smart devices with camera / camera functions such as mobile phones, digital cameras / video cameras, and monitoring equipment, as well as the rapid development of the Internet as a carrier of information sharing and dissemination, people can come into contact with a large number of How to efficiently extract useful semantic information from these image data is of great significance to the effective use of image data resources. Among them, text objects in images carry rich semantic content about images and scenes, and their effective extraction can play an important role in image analysis, understanding, classification, retrieval, recommendation and other applications. On the other hand, due to the influence of many factors such as th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/136G06T3/40G06N3/04G06N3/08
CPCG06N3/084G06T3/4053G06T7/136G06N3/045
Inventor 王雨阳苏丰
Owner NANJING UNIV