Depth image restoration algorithm

Abstract

The present invention proposes a depth image repair algorithm, which combines morphological filtering and bilateral filtering algorithm to process the depth image, and realizes the filling of holes in the depth image through several basic algorithms in morphology. The image is then smoothed using bilateral filtering. Different types of noise signals contained in the target object are eliminated by using different structural elements. The steps of the algorithm are as follows: Step 1: Establish the dilation, erosion, opening operation, and closing operation formulas of the morphological algorithm; Step 2: Establish a morphological filter; Step 3: Select structural elements; Step 4: Establish bilateral filtering criteria.

Description

technical field [0001] The invention relates to the field of depth image processing and image restoration. Background technique [0002] Depth images are widely used in the fields of human-computer interaction, 3D scene reconstruction, navigation and positioning; due to the influence of the equipment itself and the acquisition environment during the acquisition process of depth images, there are a large number of holes in the acquired images, resulting in It is difficult to extract accurate depth information. [0003] Many researchers at home and abroad have also conducted a lot of research on the optimization and repair of depth images, such as filling and repairing holes through Gaussian filtering, repairing depth images through bilateral filtering, and repairing depth images through non-local mean Filtering adds edge information weights to upsampling the depth image to repair the depth image, etc., but all methods have defects that can easily cause edge blur, loss of edg...

AI Technical Summary

Problems solved by technology

[0002] Depth images are widely used in the fields of human-computer interaction, 3D scene reconstruction, navigation and positioning; due to the influence of the equipment itself and the acquisition environment during the acquisition process of depth images, there are a large number of holes in the acquired images, resulting in Difficult to extract accurate depth information

[0003] Many researchers at home and abroad have also conducted a lot of research on the optimization and repair of depth images, such as filling and repairing holes through Gaussian filtering, repairing depth images through bilateral filtering, and repairing depth images through non-local mean Filtering adds edge information weights to upsample the depth image to repair the depth image, etc., but all methods have defects, which can easily cause edge blur, loss of edge information, regionality, and large-area holes. The effect is not very good

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