Abstract. High dynamic range images contain luminance information of the physical world and provide more realistic experience than conventional low dynamic range images. Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent. We propose a novel method for restoring the lost dynamic range from a single low dynamic range image through a deep neural network. The proposed method is the first framework to create high dynamic range images based on the estimated multi-exposure stack using the conditional generative adversarial network structure. In this architecture, we train the network by setting an objective function that is a combination of L1 loss and generative adversarial network loss. In addition, this architecture has a simplified structure than the existing networks. In the experimental results, the proposed network generated a multi-exposure stack consisting of realistic images with varying exposure values while avoiding artifacts on public benchmarks, compared with the existing methods. In addition, both the multi-exposure stacks and high dynamic range images estimated by the proposed method are significantly similar to the ground truth than other state-of-the-art algorithms.