Outlier Detection for Multi-Sensor Super-Resolution in Hybrid 3D Endoscopy

Abstract

In hybrid 3D endoscopy, range data is used to augment photometric information for minimally invasive surgery. As range sensors suffer from a rough spatial resolution and a low signal-to-noise ratio, subpixel motion between multiple range images is used as a cue for superresolution to obtain reliable range data. Unfortunately, this method is sensitive to outliers in range images and the estimated subpixel displacements. In this paper, we propose an outlier detection scheme for robust super-resolution. First, we derive confidence maps to identify outliers in the displacement fields by correlation analysis of photometric data. Second, we apply an iteratively re-weighted least squares algorithm to obtain the associated range confidence maps. The joint confidence map is used to obtain super-resolved range data. We evaluate our approach on synthetic images and phantom data acquired by a Time-of-Flight/RGB endoscope. Our outlire detection improves the median peak-signal-tonoise ratio by 1.1 dB.