Image fusion-based methods have received much attention in image processing applications in recent years. In this paper, an efficient and natural image fusion method based on the active contour model (ACM) and adaptive gamma correction (AGC) is proposed for the low-light images. The image is segmented into object and background regions quickly and detailed using hybrid ACM based on Chen-Vese and Local Gaussian Distribution Fitting (CV-LGDF), and a fusion mask is obtained. Then, the effective gamma correction parameter is calculated by using the exposure threshold independently for each region. The dynamic pixel range of each region is distributed using the histogram stretching. The color space of each region is converted to the HSI color space, and then the intensity component of each region is enhanced independently with the AGC method. The enhanced regions are merged using the fusion mask, and the color space of the enhanced image is transformed into RGB color space. Finally, histogram equalization is performed on the input image using the histogram map of the fusion image. The performance of the proposed method is compared to that of other state-of-the-art low-light methods. The experiments illustrate that our method provides effective and natural enhancement of the contrast and brightness in the image.