Image reflection removing method based on depth convolution generative adversarial network

A deep convolution and image technology, applied in biological neural network models, image enhancement, image data processing, etc., can solve problems such as large differences in natural images, unsuitable natural images, and inability to describe distribution, and achieve flexible definition capabilities. Effect

Active Publication Date: 2017-08-29
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0005] The method of Levin et al. can achieve better results on images with simple scenes and sparse textures, but it is not suitable for more general natural images with rich texture information.
This is because the differences between natural images are very

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  • Image reflection removing method based on depth convolution generative adversarial network
  • Image reflection removing method based on depth convolution generative adversarial network
  • Image reflection removing method based on depth convolution generative adversarial network

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with specific examples.

[0055] The image reflection removal method based on deep convolution confrontation generation network provided by this embodiment is as follows:

[0056] Step 1, obtain the public data set and divide it into training data set and verification data set, which are used in the model training phase and model verification phase respectively.

[0057] Step 2, preprocessing the images in the data set to meet the input requirements of the deep convolutional confrontation generation network, including the following steps:

[0058] Step 21, under the premise of maintaining the aspect ratio of the image, the image is scaled to a size where the length of the short side is 144 pixels.

[0059] Step 22, random cropping to obtain a square image with a size of 128×128 pixels.

[0060] Step 23, horizontally flip the image with a probability of 0.5.

[0061] Step 24, normalize the image from...

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Abstract

The invention discloses an image reflection removing method based on a depth convolution generative adversarial network. The method comprises the following steps: 1) data obtaining; 2) data processing; 3) model structuring; 4) loss defining; 5) model training; and 6) model verifying. In combination with the capability of a depth convolution neural network to extract high-level image semantic information and the flexible capability of the generative adversarial network to define the loss function, the method overcomes the limitations of a traditional method using low level pixel information, therefore, making the method with a better adaptive ability to the reflection removal of a generalized image.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to an image reflection removal method based on a deep convolutional confrontation generation network. Background technique [0002] When we take pictures of outdoor scenes through glass indoors, the resulting photos often contain reflected images of indoor objects. Professional photographers may choose to use professional equipment such as polarizers to solve this problem. But for the average consumer, it would be more feasible to post-process photos using reflection removal algorithms. [0003] A photo with a reflection image can be regarded as a mixed image formed by superimposing a reflection image (indoor scene) and a target image (outdoor scene). The essence of the reflection removal problem is to decompose two images from such a mixed image. This problem is obviously ill-posed, that is, for a given mixed image, the possible decomposition method is not uniq...

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Application Information

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IPC IPC(8): G06T5/00G06K9/62G06N3/08
CPCG06N3/084G06T5/001G06F18/214
Inventor 徐雪妙周乐
Owner SOUTH CHINA UNIV OF TECH
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