In this era of automation, image processing is an indispensable part of computer vision. Many computer vision approaches in the industry depend on a relatively bright environment. Under low light source conditions, the distribution of image information is too concentrated in specific intensity ranges due to the color factor of the subject itself, resulting in noise and contrast loss. Enhancing contrast is a crucial step in improving the quality of the image and showing visible details. This study proposes a method based on a convolutional neural network (CNN), using the pixel difference between paired images, called a motion matrix, as an annotation for low-contrast images. The image's motion vector is predicted after the neural network model has been trained to produce the low-contrast enhanced image. Then, the proposed model is compared with the Low-Light image Enhancement (LLNet), Multi-Scale Retinex Color Restoration (MSRCR), and Fuzzy Automatic Cluster Enhancement (FACE) approaches. The effectiveness of the proposed method was further evaluated by comparing several quality indicators, including peak signal-to-noise ratio, structural similarity, root-mean-square-error, root-mean-square-contrast and computation time efficiency.