Depth image denoising method

A technology of depth image and depth value, applied in the field of image processing, can solve the problems of blurred edge smoothness and difficulty in applying depth image, and achieve the effect of reducing edge blurring and improving the quality of depth image.

Active Publication Date: 2015-06-24
BEIJING UNIV OF TECH
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AI Technical Summary

Benefits of technology

The present technology described by this patented technique helps improve images captured from different angles without losing sharpness or shaking up any detail that may be lost during processing. It works well even when there are noisy backgrounds like rain on grassy areas.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the quality of depth imagery captured from an object's surface without losing detail that may come through various types of processing techniques like ghost detection and shading correction.

Method used

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Embodiment Construction

[0013] The denoising method of this depth image includes the following steps:

[0014] (1) Perform joint bilateral filtering on the depth image, and constrain the scope of the bilateral filter to obtain the filtered image;

[0015] (2) Use the K-SVD method to train the dictionary, and use the sparse representation based on the dictionary to denoise the filtered image in step (1), so as to obtain the reconstructed image.

[0016] Through the joint denoising method of joint bilateral filtering and dictionary sparse representation, the present invention can reduce image edge blur, is suitable for Gaussian noise denoising with non-zero mean value, and greatly improves the quality of depth images.

[0017] Preferably, the depth image in step (1) includes two kinds of noise: the first noise is the lack of depth value caused by light reflection and occlusion; Inconsistent color image shape;

[0018] For the first noise filter according to formula (1):

[0019] ...

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Abstract

The invention discloses a depth image denoising method which can reduce edge blur of images, is applicable to non-zero-mean Gaussian denoising and greatly improves depth image quality. The depth image denoising method includes the steps that 1, joint bilateral filtering is carried out on the depth images, the action scope of a bilateral filter is constrained, and accordingly filtered images are obtained; 2, a dictionary is trained through a K-SVD method, the filtered images of the first step are denoised through sparse representation on the basis of the dictionary, and reconstructed images are obtained.

Description

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Claims

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Application Information

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Owner BEIJING UNIV OF TECH
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