论文标题
根据纹理信息可见且近红外图像融合
Visible and Near Infrared Image Fusion Based on Texture Information
论文作者
论文摘要
多传感器融合被广泛用于自动驾驶汽车的环境感知系统。它解决了环境变化引起的干扰，并使整个驾驶系统更安全，更可靠。在本文中，提出了一种基于纹理信息的新型可见且近红外的融合方法，以增强非结构化的环境图像。它针对传统可见和近红外图像融合方法中的工件，信息损失和噪声问题。首先，通过相对总变化（RTV）计算，可见图像（RGB）（RGB）的结构信息（RGB）和近红外图像（NIR）作为融合图像的基层；其次，建立了贝叶斯分类模型来计算噪声重量，可见图像中的噪声信息和噪声信息通过关节双侧过滤器自适应过滤；最后，融合图像是通过颜色空间转换获得的。实验结果表明，所提出的算法可以保留光谱特征和无伪影和颜色失真的可见和近红外图像的独特信息，并且具有良好的鲁棒性以及保留独特的质感。
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.
