Laser range gating technology can break through the limitations of traditional imaging in complex environments such as rain, snow and fog, low light and inverse glare, but the generated gated image is a lowquality grayscale map, which lacks color information and is difficult to distinguish between the subject and the background, so super-resolution reconstruction technology is needed to focus on the reconstruction of edge information and spatial details to improve the visual effect. Due to the lack of color and rich texture information in the gated image, the traditional feature extraction method is prone to redundant features, which affects the reconstruction efficiency. In order to solve the above problems, this paper proposes a bi-aggregation deep feature extraction network. Firstly, shallow feature extraction was carried out by spatial and channel reconstruction convolution (SCConv) to improve the information content and solve the redundancy problem. Secondly, a new deep feature extraction module was designed to enhance the capture of the edges and details of the gated image. Finally, continuous nearest neighbor interpolation and convolution operations are used for image reconstruction, which effectively avoids the problem of artifacts. Experiments on the gated image dataset show that compared with the baseline DAT algorithm, the PNSR index of the proposed method is increased by 0.19 dB, 0.12 dB and 0.04 dB under the condition of 2 fold, 3 fold and 4 fold resolution degradation, respectively, and the SSIM is increased by 0.000 5, 0.000 8 and 0.001 0, respectively, and the results show that the proposed method can achieve better visual effects.