Saliency Map-Aided Generative Adversarial Network for RAW to RGB Mapping

RAW files are widely applied in cameras and scanners as storage because they contain original optical data. Different cameras usually process the RAW files using diverse algorithms that are incompatible. To address the issue, we propose a general transformation method for cross-camera RAW to RGB mapping based on Generative Adversarial Network (GAN). Moreover, we propose a saliency map-aided data augmentation technique and the saliency maps are produced by Saliency GAN (SalGAN). Given RAW file as an input, it jointly predicts the RGB image and corresponding saliency map to enhance perceptual quality in the generated image. The proposed architecture is trained on the Zurich RAW2RGB (ZRR) dataset. Experimental results show that our method can generate more clear and visually plausible images than state-of-the-art networks.