# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
###### Abstract  
The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify and locate the fault points of thermal hazards. Among them, a very important step is the registration of thermally visible images. However, the direct registration of images with large-scale differences may lead to large registration errors or even failure. This paper presents a novel two-stage thermal-visible-image registration strategy specifically designed for exceptional scenes, such as a substation. Firstly, the original image pairs that occur after binarization are quickly and roughly registered. Secondly, the adaptive downsampling unit partial-intensity invariant feature descriptor (ADU-PIIFD) algorithm is proposed to correct the small-scale differences in details and achieve finer registration. Experiments are conducted on 30 data sets containing complex power station scenes and compared with several other methods. The results show that the proposed method exhibits an excellent and stable performance in thermal-visible-image registration, and the registration error on the entire data set is within five pixels. Especially for multimodal images with poor image quality and many detailed features, the robustness of the proposed method is far better than that of other methods, which provides a more reliable image registration scheme for the field of fire safety. thermal hazard; thermal-visible-image registration; two-stage registration strategy +
Footnote †: journal: Medical  
or at distinct time points, may have utilized identical or diverse sensors, and may have been captured from corresponding or disparate viewpoints [5]. Generally speaking, the registration process consists of four steps: feature detection, feature matching, transform model estimation, and image resampling and transformation. The core task of image registration is to ascertain the geometric transformation function that facilitates the mapping of one image onto another. The transformation function converts the image to a reference image to align the spatial coordinates of the two images. Image registration methods are generally divided into three categories: area-based methods, feature-based methods, and deep-learning-based methods. Firstly, for the area-based method, there is no feature detection in essence, which means it does not detect the salient features in the image but directly uses the predefined window for feature matching. Common region-based methods include the following: (1) correlation-like methods [6; 7; 8; 9; 10] (2) Fourier methods [11; 12; 13; 14; 15] (3) mutual information methods [16; 17; 18; 19]; (4) optimization methods [17; 20; 21; 22; 23]. For those images whose image features are not obvious and do not have rich details, region-based image registration methods are usually used. For example, images in the medical field can selectively adopt the external features of patients through expert experience, to reduce the impact of feature loss. However, this method has many limitations: firstly, for predefined windows, only rigid transformations between images can be registered. If more complex elastic transformations occur between images, registration cannot be completed, which is also this method's inherent disadvantage; secondly, the region-based method matches the features of the entire window, and cannot detect and match the local features of different parts of the image, resulting in a poor accuracy and robustness. Finally, because the method is based on the image pixel intensity of image registration, in the process of thermal image and visible image registration, due to the lack of texture in the thermal image, the intensity mapping between the infrared image and visible-light image is very complex and difficult to predict.