Abstract:

In view of the complex and time-consuming synthetic process of the high dynamic range (HDR) images, a novel HDR image transfer technique based on the generative adversarial network has been proposed. The process is as follows: first to build two training sets of the generative adversarial network---ordinary images and low-exposure HDR images; ordinary images and high exposure HDR images. Then, through the training of the generative adversarial networks, the two generative models of ordinary images to low exposure HDR images and ordinary images to high exposure HDR images are established. Finally, a picture is put into the model, the high and low exposure images and the original images are combined to synthesize HDR files, and the tone mapping forms the image after the final HDR style transfer. This method not only solves effectively the problem of HDR image style transfer, but also proves the advantages of the generative adversarial network in processing image editing.

Key words: generative adversarial network, Gamma correction, image editing, image style transfer, deep learning