Visible and Near Infrared Image Fusion Based on Texture Information

TL;DR

This paper introduces a novel texture-based fusion method for visible and near-infrared images, improving detail preservation and reducing artifacts in multi-sensor environmental perception for autonomous vehicles.

Contribution

It proposes a new fusion approach using RTV and Bayesian classification to enhance image quality and robustness, addressing issues of artifacts and information loss in traditional methods.

Findings

Preserves spectral characteristics and textures effectively.

Reduces artifacts and color distortion.

Demonstrates robustness in various conditions.

Abstract

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.