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A spatially correlated fractional integral-based method for denoising geiger-mode avalanche photodiode light detection and ranging depth images.
- Source :
-
Optik - International Journal for Light & Electron Optics . Oct2023, Vol. 288, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) target echo signals are susceptible to interference from atmospheric backscatter, daylight, and other noise, and extracted target depth images contain a large amount of anomalous noise. Accordingly, this paper devises a method based on a spatially correlated fractional integral for denoising GM-APD LiDAR depth images. First, a multi-scale superpixel fusion algorithm is employed to perform null-pixel complementation on a target depth image extracted using histogram statistics. Next, a pixel neighborhood spatial correlation kernel function is used to optimize the Grünwald–Letnikov fractional integral operator to correct the large amount of anomalous noise and thus achieve GM-APD depth image denoising. Simulation and experimental results demonstrate that the denoising performance of the algorithm on GM-APD LiDAR depth images is significantly better than that of median filtering and bilateral filtering, with at least 22.8 % and 4.8 % improvements in the target reduction degree (K) and peak signal-to-noise ratio (PSNR), respectively. In addition, the K and PSNR reach 91.36 % and 18.0241 dB, respectively, with 25 statistical frames in an outdoor imaging experiment. This demonstrates that the algorithm can effectively denoise GM-APD LiDAR depth images with few statistical frames and thus improve the frame rate of GM-APD LiDAR imaging. [ABSTRACT FROM AUTHOR]
- Subjects :
- *OPTICAL radar
*LIDAR
*FRACTIONAL integrals
*IMAGE denoising
*SIGNAL-to-noise ratio
Subjects
Details
- Language :
- English
- ISSN :
- 00304026
- Volume :
- 288
- Database :
- Academic Search Index
- Journal :
- Optik - International Journal for Light & Electron Optics
- Publication Type :
- Academic Journal
- Accession number :
- 169967919
- Full Text :
- https://doi.org/10.1016/j.ijleo.2023.171244