The image acquisition of substations in low light environments can lead to problems such as low visual quality, loss of details, and low contrast, which in turn affect the subsequent detection and monitoring of equipment. A fusion method based on low light image enhancement and nonsubsampling contourlet transform (NSCT) and discrete cosine transform (DCT) technology is proposed in this paper. Firstly, adaptive image adjustment is performed on visible light images based on gamma parameters to enhance visibility. Then NSCT decomposes the image into high and low frequency coefficients. For high-frequency coefficients, edge information extraction based on Sobel operator is used, and for low-frequency coefficients, improved DCT-DFT is used for decomposition and integration. The decomposed amplitude spectrum and the phase spectrum are fused using contrast enhancement weighting and local energy optimization rule based on singular value decomposition (SVD), respectively. Finally, the fused image is obtained by NSCT inverse transformation. Three sets of images of common equipment in substations are used to compare the proposed method with other algorithms. The results show that this proposed method performs better in indicators such as average gradient, information entropy and mutual information.