Depth image super-resolution processing method based on deep learning

A super-resolution and deep learning technology, applied in the field of computer image processing, can solve problems such as long calculation time, artificial traces of results, and large calculation complexity, and achieve the effect of short time

Inactive Publication Date: 2018-09-04
TIANJIN UNIV
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Problems solved by technology

In the process of obtaining high-resolution depth maps by the above methods, some methods have the problem of high computational complexity a

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  • Depth image super-resolution processing method based on deep learning
  • Depth image super-resolution processing method based on deep learning
  • Depth image super-resolution processing method based on deep learning

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Embodiment Construction

[0041] In order to solve the defects of the prior art, the present invention provides a depth map super-resolution method based on deep learning, and the technical scheme adopted in the present invention is:

[0042] 1) see figure 1 , which is a flow chart of the steps of the present invention, comprising the following steps:

[0043] 11) Select a certain number of texture-rich depth images and corresponding color images from the public data set, select more than 900 images, and name each pair of depth color images the same.

[0044] 12) Data enhancement. In order to increase the data set sample, each pair of pictures is rotated by 90°, 180° and 270°, and the number of pictures is increased by 4 times.

[0045] 13) Perform data preprocessing on the obtained depth color image pair. First, the depth map is down-sampled, and then the bicubic interpolation method is used to restore the image to its original size to obtain a low-resolution depth map. Due to the relatively large...

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Abstract

The invention belongs to the field of computer image processing, and aims to provide a method capable of carrying out effective super-resolution processing on a depth image by using a corresponding high-resolution color image to obtain sharp depth image edges. Therefor, the adopted technical solution of the invention is a depth image super-resolution processing method based on deep learning. The method comprises the following steps: 1) selecting a certain number of rich-texture depth images and color images corresponding thereto from a public data set; 2) carrying out data enhancement; 3) carrying out data preprocessing on obtained depth and color image pairs; 4) designing a deep convolutional neural network structure; and 5) using the preprocessed data set to train the designed convolutional neural network, inputting the low-resolution depth image and the corresponding color image into the trained network after the convolutional neural network is trained, and outputting a depth imageafter super-resolution processing is completed at an output layer. The method is mainly applied to image processing.

Description

technical field [0001] The invention belongs to the field of computer image processing, and in particular relates to a depth map super-resolution method based on a convolutional neural network using depth color image pairs. Background technique [0002] Depth information is an important information for the visual perception of 3D objects. Applications in autonomous driving, human-computer interaction, 3D scene reconstruction, and virtual reality all rely on high-performance and high-quality depth maps. However, the current consumer-level depth cameras, including Microsoft's Kinect, Asus' Xtion Pro and other TOF cameras based on time of flight (TOF) technology, due to limitations in hardware conditions, the resolution of the obtained depth images is low. Very low, well below the resolution of a color image. This brings great limitations in practical applications, therefore, in order to utilize the depth information data more effectively, the super-resolution method of the de...

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Application Information

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IPC IPC(8): G06T3/40G06T5/50G06T7/13G06N3/04
CPCG06T3/4007G06T3/4053G06T5/50G06T7/13G06T2207/20221G06T2207/10024G06N3/045
Inventor 杨敬钰蓝浩宋晓林李坤
Owner TIANJIN UNIV
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