A Robust Depth Map Structure Reconstruction and Denoising Method Based on Guided Filters

A guided filter and guided filtering technology, applied in the field of image processing, can solve problems such as inapplicability, and achieve the effects of model stability, strong robustness, and improved consistency

Active Publication Date: 2022-02-11
XI AN JIAOTONG UNIV
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Problems solved by technology

However, this method is not suitable for depth maps containing strong noise

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  • A Robust Depth Map Structure Reconstruction and Denoising Method Based on Guided Filters
  • A Robust Depth Map Structure Reconstruction and Denoising Method Based on Guided Filters
  • A Robust Depth Map Structure Reconstruction and Denoising Method Based on Guided Filters

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Embodiment Construction

[0042] The present invention provides a robust depth map structure reconstruction and denoising method based on a guided filter, which uses the characteristics of the Guided filter to filter under different size windows, and the fitting degree of the input map to the guide map is different, and the difference is too large The area marked as a potential structural error area, and then the weight is constructed based on the iterative reweighted least squares algorithm. After the weight construction is completed, the overall solution is performed and the depth map is updated to determine whether the set number of iterations is reached. If so, the depth map is output to end the calculation. , otherwise re-detect the structural error region. The method can remove a large amount of noise and reduce the blurring of the image edge, can recover the inconsistency between the depth image and the color image, and improve the consistency of the two, thereby improving the quality of viewpoin...

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Abstract

The invention discloses a robust depth map structure reconstruction and denoising method based on a guided filter, which detects structural error areas and detects places where the input depth map has a large difference between the guided filtering of the large window and the guided filtering of the small window , since the guided filtering under a large window can have a feathering effect, while the guided filtering of a small window only plays a smoothing role, so the area with a large difference can be considered as a structural error area, marked as a potential structural error area, and then based on iterative reweighting The least squares algorithm constructs the weights. After the weights are constructed, the overall solution is performed and the depth map is updated. According to the results, it is judged whether the set number of iterations is reached. If it is reached, the depth map is output to end the calculation. Otherwise, the structural error area is detected again. The invention can suppress strong noise, repair structural error areas of the depth map and the color map, improve the consistency of the depth map and the color map, restore the correct depth map boundary, and have important guiding significance for improving the quality of the synthetic view.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for reconstructing and denoising a robust depth image structure based on a guiding filter. Background technique [0002] With the advent of depth sensors and the rapid development of stereoscopic display technology, depth maps have become a research hotspot in recent years. There are two ways to obtain the depth map: active and passive. The active method mainly uses the visible light data of a single viewpoint for depth estimation, or performs stereo matching for the visible light data of two (or more) viewpoints to calculate the disparity of the corresponding position, and then converts it into a depth map according to the geometric relationship. With the successful application of deep learning in the field of computer vision, the accuracy of actively obtained depth maps has been greatly improved. However, this type of method has high requirements...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T5/50G06T7/50
CPCG06T5/002G06T5/50G06T7/50
Inventor 杨勐陈翔光宇杰成钰郑南宁
Owner XI AN JIAOTONG UNIV
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