Multi-supervision image super-resolution reconstruction method based on generative adversarial network

A super-resolution and image technology, applied in the field of image processing, can solve the problems of super-resolution reconstruction methods such as limited effects, application limitations, model degradation data, etc., to save slice scanning time and hardware costs, and solve super-resolution reconstruction blur and artifacts Effect

Pending Publication Date: 2019-10-11
怀光智能科技(武汉)有限公司
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Problems solved by technology

In recent years, super-resolution methods based on deep learning have achieved remarkable results, but most of the models are based on degraded data, and there are certain limitations in the application of images of different resol

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  • Multi-supervision image super-resolution reconstruction method based on generative adversarial network
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  • Multi-supervision image super-resolution reconstruction method based on generative adversarial network

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[0072] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0073] The terms "first", "second" and "third" in the description and claims of the present invention are used to distinguish different objects, rather than to describe a specific order.

[0074] In the present invention, low-resolution images, intermediate-resolution images, and high-resolution images are relative concepts. For example, a 4x image is a low-resolution image relative to a 10x im...

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Abstract

The invention discloses a multi-supervision image super-resolution reconstruction method based on a generative adversarial network. The method comprises the following steps: registering slice images with different resolutions of the same slice; making a training data set by using the registered slice images; on the training data set, training a generative adversarial model by using a multi-supervision multi-stage generation model; and reconstructing the low-resolution image into a high-resolution image by using the trained generative adversarial model. According to the method, the image shot under the low-power lens is reconstructed into the high-resolution image, the imaging time can be saved, the hardware space for storing the image can also be saved, and blurring and artifacts of commonmethods on pathological data are overcome.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a multi-supervised image super-resolution reconstruction method based on a generative confrontation network, which is especially suitable for pathological slice images. Background technique [0002] At present, cell microscopic images require an objective lens with a magnification of 20x and above to be able to image clearly, but the imaging takes a long time and requires a large storage space. The imaging speed under the 4x objective lens is fast, and the requirements for equipment accuracy are low. However, the imaging resolution under the 4x objective lens is low, the depth of field is large, and the imaging is not practical. If images taken under a low magnification lens can be reconstructed into high-resolution images, both imaging time and hardware space for storing images can be saved. [0003] Some researchers at home and abroad have proposed th...

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Application Information

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IPC IPC(8): G06T3/40G06T7/33
CPCG06T3/4076G06T7/33G06T2207/10056G06T2207/20081G06T2207/20084
Inventor 程胜华曾绍群马嘉波余静雅刘秀丽余江盛
Owner 怀光智能科技(武汉)有限公司
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