Aiming at the problems of low registration and superposition accuracy of ultraviolet image and visible image and large deviation of corona discharge location in ultraviolet imager, a UV and visible image registration and fusion method was proposed based on the GoogLeNet model, Wavelet Transform (WT) and Canny operator, and it was applied in highly sensitive UV imagers. First, by introducing the idea of migration learning, the pre-trained GoogLeNet model was used to autonomously mine the characteristics of visible light and ultraviolet images. Then, the extracted features were input into extreme learning machine (ELM) as predictive variables, and supervised model training was guided by spatial transformation parameters to achieve high-precision UV and visible image registration. Finally, the multi-resolution analysis and edge detection of the registered image were carried out by using two-dimensional wavelet transform and Canny operator to realize the image fusion without UV information loss. The experimental results show that the proposed method has high registration accuracy of UV and visible images, completing good fusion effect and engineering applicability.