Adaptive switching filter for noise removal in highly corrupted depth maps from Time-of-Flight image sensors

Abstract

In this work, we present an adaptive switching filter for noise reduction and sharpness preservation in depth maps provided by Time-of-Flight (ToF) image sensors. Median filter and bilateral filter are commonly used in cost-sensitive applications where low computational complexity is needed. However, median filter blurs fine details and edges in depth map while bilateral filter works poorly with impulse noise present in the image. Since the variance of depth is inversely proportional to amplitude, we suggest an adaptive filter that switches between median filter and bilateral filter based on the level of amplitude. If a region of interest has low amplitude indicating low confidence level of measured depth data, then median filter is applied on the depth at the position while regions with high level of amplitude is processed with bilateral filter using Gaussian kernel with adaptive weights. Results show that the suggested algorithm performs surface smoothing and detail preservation as well as median filter and bilateral filter, respectively. By using the suggested algorithm, significant gain in visual quality is obtained in depth maps while low computational cost is maintained.