|Title:||Temporal and spatial denoising of depth maps||Authors:||Lin, B.-S.
|Keywords:||Depth image; Hole padding; RGB-D sensor; Spatial-temporal denoising||Issue Date:||2015||Journal Volume:||15||Journal Issue:||8||Start page/Pages:||18506-18525||Source:||Sensors (Switzerland)||Abstract:||
This work presents a procedure for refining depth maps acquired using RGB-D (depth) cameras. With numerous new structured-light RGB-D cameras, acquiring high-resolution depth maps has become easy. However, there are problems such as undesired occlusion, inaccurate depth values, and temporal variation of pixel values when using these cameras. In this paper, a proposed method based on an exemplar-based inpainting method is proposed to remove artefacts in depth maps obtained using RGB-D cameras. Exemplar-based inpainting has been used to repair an object-removed image. The concept underlying this inpainting method is similar to that underlying the procedure for padding the occlusions in the depth data obtained using RGB-D cameras. Therefore, our proposed method enhances and modifies the inpainting method for application in and the refinement of RGB-D depth data image quality. For evaluating the experimental results of the proposed method, our proposed method was tested on the Tsukuba Stereo Dataset, which contains a 3D video with the ground truths of depth maps, occlusion maps, RGB images, the peak signal-to-noise ratio, and the computational time as the evaluation metrics. Moreover, a set of self-recorded RGB-D depth maps and their refined versions are presented to show the effectiveness of the proposed method. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
|ISSN:||14248220||DOI:||10.3390/s150818506||SDG/Keyword:||Cameras; Image quality; Signal to noise ratio; Computational time; De-noising; Depth image; High-resolution depth; Hole padding; Peak signal to noise ratio; Rgb-d sensors; Temporal and spatial; Stereo image processing; algorithm; automated pattern recognition; data base; procedures; signal noise ratio; three dimensional imaging; time factor; Algorithms; Databases as Topic; Imaging, Three-Dimensional; Pattern Recognition, Automated; Signal-To-Noise Ratio; Time Factors|
|Appears in Collections:||電機工程學系|
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