Abstract: In order to solve the visual perception problem of unmanned vehicles caused by uneven light in tunnels, an enhancement method based on the fusion of active infrared and visible light was proposed. By supplementing image texture and detail information, the impact of tunnel light fluctuations on unmanned driving was improved. Firstly, the image was denoised by pilot filter, and the visual information in the tunnel under different illuminance was enhanced by contrast limit adaptive histogram equalization. Secondly, the enhanced infrared and visible images were decomposed by using the non-subsampling contour wave method. The fusion rules of regional energy adaptive weighted average and convolutional sparse representation were used for low frequency base and detail respectively, and the significance fusion rules were applied to the directional components of high frequency details. Finally, the high and low frequency fusion components were reconstructed. Experimental results show that the average time of CSR-RE algorithm based on convolutional sparse representation and regional energy adaptive weighted average is reduced by 0.05 s compared with deep learning framework (DLF) algorithm. Compared with the fusion method based on sparse representation, the mutual information (MI) value of CSR-RE algorithm is increased by 1.5, and the spatial frequency (SF) value is increased by 0.51 at most. The overall performance of CSR-RE algorithm is better than that of other algorithms.