When the image enhancement method LLNet (the low-light net) was applied to three-channel images, there're a lot of redundant parameters. To solve this problem, a framework called CAENet (convolutional auto-encoder network) was proposed. Firstly, CAENet combined a low light processing module with a network training module. Secondly, in the encoding and decoding stages, CAENet used a convolutional network to replace the traditional fully connected network. The experimental results show that connecting low-light processing modules with network training can effectively save time costs. At the same time, the use of convolutional networks can reduce network parameters, making network training more efficient, and obtain better low-dimensional representation of images. The experimental results on the Corel5k dataset show that CAENet can effectively improve the image light perception and color perception while reducing network parameters. The experimental results on high-resolution datasets show that CAENet can preserve details for image details without distortion. In addition, for the noisy low-light image, CAENet can enhance the image while achieving the denoising effect, which proves that CAENet has strong robustness.