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Multi-sensor fusion is widely used in the environment perception system ofthe autonomous vehicle. It solves the interference caused by environmentalchanges and makes the whole driving system safer and more reliable. In thispaper, a novel visible and near-infrared fusion method based on textureinformation is proposed to enhance unstructured environmental images. It aimsat the problems of artifact, information loss and noise in traditional visibleand near infrared image fusion methods. Firstly, the structure information ofthe visible image (RGB) and the near infrared image (NIR) after texture removalis obtained by relative total variation (RTV) calculation as the base layer ofthe fused image; secondly, a Bayesian classification model is established tocalculate the noise weight and the noise information and the noise informationin the visible image is adaptively filtered by joint bilateral filter; finally,the fused image is acquired by color space conversion. The experimental resultsdemonstrate that the proposed algorithm can preserve the spectralcharacteristics and the unique information of visible and near-infrared imageswithout artifacts and color distortion, and has good robustness as well aspreserving the unique texture.

Visible and Near Infrared Image Fusion Based on Texture Information