Image reflection removing method based on depth convolution generative adversarial network

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...

AI Technical Summary

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 large, and their distribution cannot be simply described by a unified histogram

The methods of Li et al. and Shih et al. are aimed at images taken under specific conditions, which are not widespread, so the usage scenarios of these methods are very restrictive.

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