Proc. SPIE. 9393, Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015
KEYWORDS: Cameras, Digital filtering, Denoising, Interference (communication), Head, Time of flight cameras, Image filtering, Phase measurement, RGB color model, Picture Archiving and Communication System
This paper presents a new pre-processing algorithm for Time-of-Flight (TOF) depth map denoising. Typically, denoising algorithms use the raw depth map as it comes from the sensor. Systematic artifacts due to the measurement principle are not taken into account which degrades the denoising results. For phase measurement TOF sensing, a major artifact is observed as salt-and-pepper noise caused by the measurement’s ambiguity. Our pre-processing algorithm is able to isolate and unwrap affected pixels deploying the physical behavior of the capturing system yielding Gaussian noise. Using this pre-processing method before applying the denoising step clearly improves the parameter estimation for the denoising filter together with its final results.
While Denoising is an extensively studied task in signal processing research, most denoising methods are designed and evaluated using readily processed image data, e.g. the well-known Kodak data set. The noise model is usually additive white Gaussian noise (AWGN). This kind of test data does not correspond to nowadays real-world image data taken with a digital camera. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or suboptimal choices in research on real-time camera denoising algorithms. In this paper we derive a precise analysis of the noise characteristics for the different steps in the color processing. Based on real camera noise measurements and simulation of the processing steps, we obtain a good approximation for the noise characteristics. We further show how this approximation can be used in standard wavelet denoising methods. We improve the wavelet hard thresholding and bivariate thresholding based on our noise analysis results. Both the visual quality and objective quality metrics show the advantage of the proposed method. As the method is implemented using look-up-tables that are calculated before the denoising step, our method can be implemented with very low computational complexity and can process HD video sequences real-time in an FPGA.