Image restoration method based on adversarial generative network

A repair method and network technology, applied in biological neural network models, image enhancement, image analysis, etc., to achieve a clear repair effect and ensure no distortion.

Active Publication Date: 2020-09-01
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0011] There are still deficiencies in the existing image...

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  • Image restoration method based on adversarial generative network
  • Image restoration method based on adversarial generative network
  • Image restoration method based on adversarial generative network

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Embodiment Construction

[0058] In order to make the objectives, technical solutions and effects of the present invention clearer and clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0059] Please refer to Figure 1-Figure 6 , The present invention provides an image restoration method based on a confrontation generation network, which includes the steps:

[0060] S1. Construct an image restoration training network: add the generation SE-RestNet network to the generation network, and add the discriminant SE-RestNet network to the discriminant network to obtain the image restoration training network;

[0061] S2. Training: extracting several original images from the training data set, masking processing to obtain several training images, respectively using the generating...

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Abstract

The invention relates to an image restoration method based on an adversarial generative network, and the method comprises the steps: S1, constructing an image restoration training network: adding a generated SE-RestNet network into a generative network, adding a discrimination SE-RestNet network into a discrimination network, and obtaining an image restoration training network; S2, conducting training: extracting a plurality of original images from a training data set; performing mask processing to obtain a plurality of training images, using the generated SE-RestNet network in the generated network to generate a training restoration image, and then using the discrimination SE-RestNet network in the discrimination network to discriminate whether the restored image is true or false, and after the discrimination network reaches a balance state, using the trained generation network as an image restoration network. According to the image restoration method based on the generative adversarial network provided by the invention, an SE-RestNet network block is added into the generative adversarial network, so that the restored image ensures the image structure and semantic coherence, the image restoration effect is better, and no restoration trace exists.

Description

Technical field [0001] The invention relates to the field of image modification, in particular to an image restoration method based on a confrontation generation network. Background technique [0002] Image restoration is an image processing technology that uses existing information in the image to repair missing information in the image or remove specific information in the image under the premise of ensuring the clarity of the image and its semantic continuity. The core challenge of this technology is to synthesize visually realistic and semantically reasonable pixels for the missing areas so as to be consistent with existing pixels. Image restoration has important practical significance, especially in the protection of works of art, restoration of old photos, image-based rendering and computer photography. [0003] Traditional image restoration methods mostly use image-level features to deal with restoration problems. Patch-Match method proposes to find the best matching patch...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/005G06N3/08G06T2207/10004G06T2207/20081G06N3/045Y02T10/40
Inventor 陈沅涛张浩鹏蔡烁余飞陈曦王震陶家俊刘林武王柳吴一鸣
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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