When the light source is located on the back of the object, the captured image has a dark foreground and a bright background. This non-uniform light distribution significantly affects the overall visual quality of the image. As existing image enhancement methods have difficulty in effectively solving the non-uniform illumination problem of backlit images, this study proposes a backlight image enhancement network guided by an attention mechanism. First, a U-shaped network is used to construct an enhancement subnetwork (EM-Net), to achieve multiscale feature extraction and reconstruction. Second, a condition subnetwork (Cond-Net) is introduced to generate a backlight area attention map to guide the EM-Net to focus on the backlight area in the image. Then, using a dual-branch enhancement block (DEB), the brightness of the backlight area is fully enhanced while maintaining the contrast of the front-light area. In addition, a spatial feature transformation (SFT) layer is introduced in the backlight branch of the DEB, allowing EM-Net to focus on improving the visibility of the backlight area according to the guidance provided by the area attention map. Finally, to strengthen the correlation between the backlight and front-light areas during the enhancement process, a bilateral mutual attention module (BMAM) is proposed to further improve the reconstruction ability of the EM-Net. Experimental results show that the peak signal to noise ratio (PSNR) metric obtained by the proposed algorithm on the backlight data set (BAID) and non-uniform exposure local color distribution prior dataset (LCDP) exceeds that obtained by the latest backlight image enhancement contrastive language-image pretraining (CLIP)-LIT algorithm by 3.15 dB and 4.81 dB, respectively. Compared with other image enhancement algorithms based on deep learning, the proposed algorithm can effectively improve the visual quality of backlit images with higher computing efficiency.