A depth image super-resolution reconstruction network (DF-Net) based on dual feature fusion guidance is proposed to address the issues of texture transfer and depth loss in color image guided deep image super-resolution reconstruction algorithms. To fully utilize the correlation between depth and intensity features, a dual channel fusion module (DCM) and a dual feature guided reconstruction module (DGM) are used to perform deep recovery and reconstruction in the network model. The multi-scale features of depth and intensity information are extracted using a input pyramid structure: DCM performs feature fusion and enhancement between channels based on a channel attention mechanism for depth and intensity features; DGM provides dual feature guidance for reconstruction by adaptively selecting and fusing depth and intensity features, increasing the guidance effect of depth features, and overcoming the issues of texture transfer and depth loss. The experimental results show that the peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) of the proposed method are superior to those of methods such as RMRF, JBU, and Depth Net. Compared to the other methods, the PSNR value of the 4× super-resolution reconstruction results increased by an average of 6.79 dB, and the RMSE decreased by an average of 0.94, thus achieving good depth image super-resolution reconstruction results.