Photograph enhancement via imitation-to-innovation training scheme

Post processing is usually necessary for photographs captured in undesired situations to improve their visual quality. As photo retouching requires operators to receive professional training, automatic photograph enhancing system would benefit amateur photographers and also save experts from tedious retouching tasks. In this paper, we present a deep learning framework based on imitation-to-innovation training scheme. Our method integrates the bilateral grid data structure and Generative Adversarial Network (GAN) to achieve high time-efficiency and appealing retouching output. Besides, we present bilateral loss function to maintain piecewise smoothness. The experimental results and user study demonstrate that our method produces improved results than other state-of-the-art methods.