Jiang Yingqi, Dong Lili, Liang Junke
School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel). 2022 Aug 5;22(15):5873. doi: 10.3390/s22155873.
Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.
红外图像增强技术能够有效提升图像质量，增强目标的显著性，是海洋目标搜索与跟踪系统中的关键组成部分。然而，海上红外图像的成像质量容易受到天气和海况的影响，存在对比度低和目标轮廓信息薄弱的缺陷。同时，目标受到不同强度海杂波的干扰，目标特征也各不相同，无法用单一算法进行处理。针对这些问题，深入分析了目标的方向纹理特征与海面粗糙度之间的关系。根据海浪的纹理粗糙度，将图像场景自适应地划分为平静海面和粗糙海面。同时，通过特定频率的Gabor滤波器和本文提出的基于梯度的目标特征提取算子，制定了杂波抑制和特征融合策略，得到了两种场景下多尺度融合的目标特征图像，将其作为引导滤波的引导图像。将原始图像分解为目标层和背景层，以提取目标特征并避免图像失真。基于潜在目标区域通过高斯滤波提取目标轮廓周围模糊的背景，消除目标热传导引起的边缘模糊。最后，通过对目标层和背景层进行适当加权融合得到增强图像。实验结果表明，与当前图像增强方法相比，本文提出的方法能够提高图像的清晰度和对比度，增强遇险目标的可探测性，去除海面杂波的同时保留背景中的自然环境特征，为海上搜救中的目标检测和持续跟踪提供更多信息。
