Image depth estimation method based on convolutional neural network

A convolutional neural network and image depth technology, which is applied in the field of image depth estimation based on convolution-deconvolution neural network, can solve problems such as blurred edges of depth maps, inaccurate depth values, and weak sense of layering in depth maps , to avoid model inaccuracy, enhance learning ability, improve PNSR and visual effects

Active Publication Date: 2018-03-06
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Such an estimation method usually estimates inaccurate depth values, and the depth map layering is not strong.
At the same ti

Method used

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  • Image depth estimation method based on convolutional neural network
  • Image depth estimation method based on convolutional neural network
  • Image depth estimation method based on convolutional neural network

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Embodiment

[0030] This embodiment provides a method for estimating image depth based on a convolutional neural network. The neural network of the method introduces a convolution-deconvolution layer pair, a convolution layer, and an activation layer, with the help of the learning ability and the activation layer of the convolution layer. The screening ability of the activation layer can obtain good features, which greatly enhances the learning ability of the neural network, and accurately learns the mapping from the original image to the depth image to establish the mapping from input to output, so that the depth image can be processed through the learned mapping. predictions and estimates. Flowchart such as figure 1 shown, including the following steps:

[0031] S1, build convolution-deconvolution pair neural network model, described convolution-deconvolution pair neural network model includes a plurality of different convolution layers, a plurality of convolution-deconvolution layer pa...

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Abstract

The invention discloses an image depth estimation method based on a convolutional neural network. The method comprises the following steps: constructing a convolution-deconvolution pair neural networkmodel, wherein the convolution-deconvolution pair neural network model comprises a plurality of different convolutional layers, a plurality of convolution-deconvolution layer pairs and an activationlayer; selecting a training set, and setting training parameters of the convolution-deconvolution pair neural network model; according to the convolution-deconvolution pair neural network model and the training parameters thereof, training the convolution-deconvolution pair neural network model with minimizing of a loss function being a target to form an image depth estimation neural network model; and inputting an image to be processed to the image depth estimation neural network model and outputting a corresponding depth image. The grayscale value of the depth image obtained through the image depth estimation method based on the convolution-deconvolution pair neural network is more accurate, and the sense of depth of the depth image is higher.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to an image depth estimation method based on convolution-deconvolution neural network. Background technique [0002] The depth estimation method is used to estimate the depth information of each pixel in the image to be processed, and obtain the global depth map of the image to be processed, which plays an important role in the application fields of computer vision and computer graphics. Current depth estimation methods can be divided into monocular and binocular based on the number of cameras. [0003] Binocular stereo vision uses two cameras to observe the same object from two viewpoints, obtains perception images under different perspectives of the object, and converts the disparity information of matching points into depth by triangulation. The general binocular vision method uses the epipolar geometry to transform the problem into the Euclidean geometry con...

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Application Information

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IPC IPC(8): G06T7/50
Inventor 李格余翔宇
Owner SOUTH CHINA UNIV OF TECH
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