Stereo matching for infrared images using guided filtering weighted by exponential moving average

Abstract

Infrared imaging is less susceptible to illumination conditions and haze than visible light imaging. The advantage makes infrared sensing suitable for providing remote visibility with reduced distortion. However, infrared images tend to have low resolution and lack rich textures that facilitate stereo matching. To enhance the applicability of infrared stereo imaging, the authors re-examine the guided-image techniques to include advanced edge-aware filters for aggregation and propose a novel guided-image filtering scheme here. Based on the exponential moving average, the weights are recursively calculated such that all pixels on the infrared image pair can contribute to a discrepancy cost. The arrangement allows additional pixels to be involved in the cost aggregation to reduce the demand for rich texture. Experimental results using the colour and thermal stereo (CATS) benchmark testbed demonstrate that the proposed approach outperforms several state-of-art approaches in generating accurate disparity maps.