Film-GAN: towards realistic analog film photo generation

In recent years, the art of film photography has reemerged as a topic of interest for both researchers and the community. Unlike digital photography, which relies on pixels to capture and store information, film photography employs silver halide to capture the scene. This process imbues film photos with a unique colour and textured graininess not present in digital photography. In this paper, we propose Film-GAN, the first Generative Adversarial Network (GAN)-based method for translating digital images to film. Film-GAN generates a corresponding film transformation of the input image based on the desired reference film style. To improve the realism of the generated images, we introduce the colour-noise-encoding (CNE) network, which extracts the colour and graininess of the reference image separately. Our experimental simulations demonstrate that Film-GAN outperforms other state-of-the-art approaches on multiple datasets. Based on evaluations from both professional photographers and amateur photography enthusiasts, the images generated by Film-GAN also received a higher number of votes, indicating its ability to produce better film-effect images.