  Multi-sensor fusion is widely used in the environment perception system of
the autonomous vehicle. It solves the interference caused by environmental
changes and makes the whole driving system safer and more reliable. In this
paper, a novel visible and near-infrared fusion method based on texture
information is proposed to enhance unstructured environmental images. It aims
at the problems of artifact, information loss and noise in traditional visible
and near infrared image fusion methods. Firstly, the structure information of
the visible image (RGB) and the near infrared image (NIR) after texture removal
is obtained by relative total variation (RTV) calculation as the base layer of
the fused image; secondly, a Bayesian classification model is established to
calculate the noise weight and the noise information and the noise information
in the visible image is adaptively filtered by joint bilateral filter; finally,
the fused image is acquired by color space conversion. The experimental results
demonstrate that the proposed algorithm can preserve the spectral
characteristics and the unique information of visible and near-infrared images
without artifacts and color distortion, and has good robustness as well as
preserving the unique texture.
