Aiming at the problems of abnormal illumination and unobvious main features of the tower in the low-quality inspection image taken by UAV, an image enhancement method based on wavelet transform and improved Retinex-Net is proposed. Firstly, the low-quality image is decomposed into low-frequency images by wavelet transform, and the low-frequency images are processed by the improved Retinex-Net network. ASPP module and SE module are introduced to enhance the network feature extraction ability, and the feature map is scaled by using jump link structure and nearest neighbor interpolation method to reduce background noise interference. The contrast-limited adaptive histogram equalization algorithm (CLAHE) is used to enhance high-frequency images and reduce high-frequency noise interference. Finally, the enhanced low-quality image is obtained by wavelet reconstruction. Experimental results on self-built data sets show that, compared with HE and MSRCR algorithms, the proposed algorithm can enhance the edge features of image details, restore the image color, improve the resolution, help transmission line operators to monitor and improve the accuracy of analysis.