Depth image super-resolution reconstruction method based on deep learning

A technology of super-resolution reconstruction and depth image, applied in the field of computer image processing, which can solve the problems of general reconstruction effect, limited information utilization, and low resolution.

Active Publication Date: 2019-07-16
XIHUA UNIV
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The multi-depth image fusion super-resolution reconstruction method only uses the internal information of the depth imag

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

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[0052] In order to solve the shortcomings of the prior art, the present invention provides a deep learning-based super-resolution reconstruction method for depth maps. The technical solutions adopted by the present invention are:

[0053] 1. Refer to figure 1 , Which is a flow chart of the steps of the present invention, when the upsampling factor is 2, the following steps are included:

[0054] (1) A certain number of depth images are selected from different depth image public data sets, 102 images are selected, and the images with larger resolution in the public data set are selected.

[0055] (2) Data enhancement. In order to increase the training set samples, each picture is rotated by 90°, 180°, 270°, and then scaled by 0.8 and 0.9 times. After the enhancement, the number of pictures is increased to 12 times. At this time, a total of 1224 images are obtained. The final training set.

[0056] (3) Preprocess the depth images in the training set obtained. Because the image size a...

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Abstract

The invention discloses a depth image super-resolution reconstruction method based on deep learning, and the method comprises the steps: training the whole network when an upsampling factor r is equalto 2, and comprises the steps: selecting a certain number of depth images from different depth image public data sets; and data enhancement: designing a deep convolutional neural network structure: training the whole network by using the processed network input data and the data label, inputting the low-resolution depth image into the trained network model after the training is completed, and outputting the depth image of which the super-resolution is completed on an output layer. According to the method, the high-dimensional feature map is generated through simultaneous training of multiplechannels of the convolutional neural network, the accurate pixel value of the original low-resolution image is reserved, and the training and convergence speed of the whole network is increased.

Description

technical field [0001] The invention belongs to the field of computer image processing, in particular to a depth image super-resolution reconstruction method based on deep learning. Background technique [0002] In recent years, due to the development of computer vision technology, the acquisition and processing of depth information has become one of the hot research directions. Different from the traditional two-dimensional color picture, the depth image contains the depth information of the scene, and intuitively reflects the geometric shape of the visible surface of the scene and the distance from the object to the camera through the pixel value. Therefore, depth images can be widely used in 3D reconstruction, human recognition, robot navigation, cultural relic protection, human-computer interaction and other fields. Currently, depth image super-resolution reconstruction methods are mainly divided into three categories: color image-guided depth image super-resolution rec...

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Application Information

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IPC IPC(8): G06T3/40G06N3/04
CPCG06T3/4053G06N3/045
Inventor 董秀成范佩佩李滔任磊李亦宁金滔
Owner XIHUA UNIV
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