A 3D denoising algorithm based on photon-counting imaging at low light level
| Autor: | Qian Chen, Guohua Gu, Weiji He, Changqiang Wu |
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| Rok vydání: | 2018 |
| Předmět: |
Pixel
Computer science Noise (signal processing) business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Signal Photon counting 010309 optics Sampling (signal processing) Computer Science::Computer Vision and Pattern Recognition Histogram 0103 physical sciences Computer vision Time domain Artificial intelligence 0210 nano-technology business Raster scan ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Tenth International Conference on Digital Image Processing (ICDIP 2018). |
| Popis: | The active 3D lidar imaging system usually spends a long time sampling many points for each spatial pixel in the target scene by raster scanning and generating a statistic histogram of photon counting. By relying on a variety of effective imaging algorithms, it extracts the depth, reflectivity and other information of target to reconstruct the 3D scene image. Since signal photons will be clustered together near the truth depth, so we set a window to gather reflected signal photons. We propose a new denoising algorithm based on photon-counting without generating photon counting statistic histogram in order to get 3D image of targets quickly. To validate the new theory in this paper, we designed a contrast test. Experimental results demonstrate that this imaging method can suppress the noise while acquiring the scene depth and reduce the sampling time at low light level. The imaging accuracy of our method is increased by over 6-fold more than the maximum likelihood estimation and improving imaging performance significantly. |
| Databáze: | OpenAIRE |
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