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Fabric image super-resolution reconstruction method

A super-resolution reconstruction and high-resolution technology, applied in the field of fabric image super-resolution reconstruction, fabric image super-resolution reconstruction based on deep learning, can solve problems such as application limitations, difficult to find joint probability, etc., to achieve ideal robustness The effect of flexibility, richness of detail and wide applicability

Pending Publication Date: 2020-04-14
WUHAN TEXTILE UNIV
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Problems solved by technology

The Markov random field model is aimed at modeling spatial relationships, but it is not easy to calculate the joint probability based on the conditional probability. The conditional probability itself is subject to unknown and highly restrictive conditions, so the application is greatly restricted

Method used

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  • Fabric image super-resolution reconstruction method
  • Fabric image super-resolution reconstruction method
  • Fabric image super-resolution reconstruction method

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Experimental program
Comparison scheme
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Embodiment

[0041] The present invention comprises the following steps:

[0042] Step 1: Obtain several images of dyed fabrics of various colors from the multi-light source light box;

[0043] Step 2: Downsample all the colored textile images in MATLAB, use the high-resolution colored textile images as the high-resolution image data set HR, and use the low-resolution colored textile images as the low-resolution image data set LR, the method of downsampling is the nearest neighbor interpolation method, the formula is as follows:

[0044]

[0045]In the formula, h and w are the height and width of the original image, (x, y) are the pixel values ​​on the original image, H and W are the height and width of the target image, and (X, Y) are the pixel values ​​on the target image .

[0046] The pixel point (X, Y) on the target image corresponds to the position (x, y) of the pixel point on the original image.

[0047] Step 3: Using the basic architecture of the generative confrontation netw...

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Abstract

The invention belongs to the field of deep learning, relates to a fabric image super-resolution reconstruction method, and is widely applied to the fields of color analysis, texture structure research, pattern recognition and the like. The method comprises the following steps: grouping and down-sampling acquired images, then establishing an SRGAN network by using basic structures of a GAN and a Resnet50 network, and then training the network in three steps by using a preprocessed fabric image as input and testing by using the test data set after the training is finished and evaluating the network performance by using the PSNR. According to the method, the resolution of the fabric image can be greatly improved, detail parts are richer and the method has ideal robustness and wide applicability.

Description

technical field [0001] The invention relates to a fabric image super-resolution reconstruction method, belongs to the field of deep learning, and is a fabric image super-resolution reconstruction method based on deep learning. Background technique [0002] In the study of fabric texture structure, due to limited equipment or irresistible factors, the collected fabric images may be blurred, and effective information cannot be accurately extracted. However, image super-resolution reconstruction technology uses a set of low-quality, low-resolution images to Generate a single high-quality, high-resolution image, which makes the image clearer and facilitates the extraction of effective information. At present, image super-resolution reconstruction has been widely used in the fields of military, remote sensing, medicine, public safety and computer vision. Image super-resolution reconstruction techniques are mainly divided into two categories: reconstruction-based methods and lear...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06T3/4046G06N3/08G06N3/045
Inventor 袁理谷迁
Owner WUHAN TEXTILE UNIV
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