Iterative depth map structure restoration method for SSIM structural similarity based on RGB-D

Abstract

The invention discloses an iterative depth map structure restoration method for SSIM structural similarity based on RGB-D. The method includes: firstly, detecting the edge of an input depth map; and expanding the edge, marking the expanded area as a potential structure distortion area; judging whether each pixel point in the potential structure distortion area is distorted or not; generating a structural distortion measurement index; and enabling distortion pixel points to construct a recovery weight by adopting a product of a color image Gaussian weight and a structural distortion measurementindex, carrying out guide recovery through weighted median filtering, then carrying out guide filtering on a distortion region, continuously carrying out iteration on a finished result image according to the steps until a set iteration termination condition is met, and outputting a depth image to finish calculation. According to the method, iterative detection and recovery are carried out on thestructure distortion area of the depth map, so that relatively accurate structure information is obtained, denoising and edge preserving are carried out on the undistorted area of the structure, and finally, the depth map with a clear structure and a smooth depth value can be obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an iterative depth map structure restoration method based on RGB-D SSIM structure similarity. Background technique [0002] Depth data plays an important role in 3D vision systems, and it can be widely used in various fields such as 3D reconstruction, robot navigation, 3D tracking, unmanned driving, and virtual reality. However, due to the immature depth perception technology at this stage, the depth data obtained by the depth sensor is seriously distorted, so it is necessary to use computing methods to repair the depth map data. The acquisition of depth map data is mainly divided into two methods: active and passive: the passive method is to obtain the corresponding depth image by performing stereo matching on binocular or multi-eye color images. In the past two decades, the passive method has made great progress, but its handling of weak textures and occlu...

AI Technical Summary

Problems solved by technology

However, their recovery of the depth map structure is still poor, and they can only work correctly under the condition that the structure distortion is not serious or even has no structure distortion.

A recently proposed depth image restoration method based on the inconsistent area detection model between the depth image and the RGB image can better restore the structural distortion of the depth image, but it cannot complete the processing and restoration work well when the depth image contains severe structural distortion.

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