In the process of colorization of NIR images, there are large modal differences between NIR images and visible images in poor images domain styles, which lead to color texture mismatch in the colorization results. The CycleGAN network is improved, a concatenated module called the Dilated Cascade Block is designed by taking advantage of the cascade structure and the Dilated convolution block. This module adopts the encode and decode cascade structure to replace the one-way connection structure in the original model residual network. A Dilated Convolution module is introduced into the coding-decoding cascade layer to further extract feature information of NIR images of different scales by utilizing the advantage of cavity convolution without losing texture details of the images. Finally, NIR of colorful images are obtained by decoding the NIR gray images. The algorithm uses the Dilated cascade method to solve the texture mismatch problem in the generation network. The perceptive loss function is used to improve the slow convergence of discriminant networks. Validation and analysis are carried out on NIR_VIS dataset. Experimental results show that the proposed method can improve the structure and color texture of the original object better, and effectively improve the visualization effect of NIR images.