Depth image denoising and enhancing method based on deep learning

Abstract

The present invention discloses a depth image denoising and enhancing method based on deep learning. The method comprises the steps of establishing a depth image denoising and enhancing convolutional neural network, wherein the network is composed of three layers of convolution units which finish the functions of feature extraction, non-linear mapping and image reconstruction of the input images respectively; jointly using the depth and visual images as the input of the convolutional neural network, wherein firstly the visual images are processed into the grayscale images in a grayscale processing manner; and enhancing the edge information and taking out the redundant information by the image preprocessing; segmenting the depth images into the image blocks according to certain intervals, adding the effective data by a rotation and pixel overturning data amplification method, and discarding the interference blocks and the redundant blocks; and improving the learning efficiency of the network adaptively based on a loss training depth image enhancement convolutional neural network of a weight map. According to the method of the present invention, the black spot filling and the denoising operations can be carried out on the depth images with noise real-timely, and the good visual effect and depth value recovery effect can be realized.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for denoising and enhancing deep images based on deep learning. Background technique [0002] With the development of portable and affordable depth cameras, the basic research and application of depth images in the field of image processing has become more and more important. By applying information on depth images, the performance of related research and applications in the field of machine vision, such as image segmentation, object tracking, image recognition, and image reconstruction, can be improved. [0003] However, due to the limitations of existing depth camera technology principles, the quality of the depth image obtained from it is not as good as the visual image, and there is a lot of noise interference, usually some random noise and "black holes" of different shapes on the edges of objects and black surfaces. , that is, the area where the depth information is...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, provide a depth image denoising and enhancement method that meets real-time requirements, and can effectively solve the problem of slow depth image denoising and enhancement processing speed and poor depth value recovery effect The problem

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