A face reticulate pattern stain removal method based on a multi-task full convolutional neural network

A convolutional neural network and multi-task technology, which is applied in the field of multi-task end-to-end netting stain removal, can solve the problems of strong ambiguity and intractability of stains, and achieve less learning content, easy training, and less learning content effect

Active Publication Date: 2019-05-24
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The difficulty of stain removal lies in the fuzziness that comes with the removal process and the intractability of strong stains

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  • A face reticulate pattern stain removal method based on a multi-task full convolutional neural network
  • A face reticulate pattern stain removal method based on a multi-task full convolutional neural network
  • A face reticulate pattern stain removal method based on a multi-task full convolutional neural network

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Embodiment Construction

[0053] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0054] The technical scheme that the present invention solves the problems of the technologies described above is:

[0055] The present invention provides a multi-task fully convolutional neural network face reticulate stain removal algorithm, the schematic flow chart of which is as follows figure 1 As shown, it specifically includes the following steps:

[0056] Step 1, using the clear face image of the public face data set CelebA as the non-reticulate image data set, based on the data, making a training set and a verification set for model training and evaluation;

[0057] Step 2, cutting the textured image, the real image, and the textured binary mask image into image blocks with a size of 64x64, and ...

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Abstract

The invention requests to protect a face reticulate pattern stain removal method based on a multi-task full convolutional neural network, and the method comprises the following specific steps: 1, taking a clear face image of a public face data set CelebA as a reticulate pattern-free image data set, and making a training set and a verification set; 2, cutting the reticulate pattern image, the realimage and the reticulate pattern binary mask image into image blocks with the size of 64*64; Step 3, obtaining detail layer information of the human face reticulate pattern image as model input by applying guide filtering; 4, designing a full convolutional neural network, and outputting residual error information and binary mask reticulate pattern discrimination information; 5, optimizing the lossof the real image, the reticulate pattern image and the residual error information by using the paired training data, predicting the loss of the reticulate pattern mask and the real reticulate pattern mask label, and training the model; and 6, predicting a test image of a real scene by using the model parameters obtained by training to obtain a predicted decontamination image. According to the invention, a high-quality image is provided for the subsequent operation of the face image.

Description

technical field [0001] The invention belongs to a convolutional neural network and image stain removal technology, in particular to a multi-task end-to-end network stain removal method based on a convolutional neural network. Background technique [0002] Image inpainting is one of the important issues in the field of image algorithms at present, and the effect of image inpainting greatly affects the image recognition results in some scenarios. Image restoration includes image completion, image smudge removal, image super-resolution, etc. For image smudge processing, the existing smudges may affect the semantics represented by the entire image or interfere with the local target of the image. Then, it is necessary to remove the smudges from the image to ensure that the subsequent recognition or detection has a higher accuracy. accuracy. [0003] With the rise of convolutional neural networks in 2012, CNN once again set off a wave of deep learning and artificial intelligence...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06K9/00G06N3/04
Inventor 陈乔松申发海陶亚弓攀豪曹依依董广县蒲柳
Owner CHONGQING UNIV OF POSTS & TELECOMM
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