Efficient Multi-Objective Gans for Image Restoration

Generative adversarial networks (GANs) have been widely adopted in many image processing tasks including restoration. In order to further improve quality of generated images, the training objective function needs to incorporate more constraints in addition to the adversarial loss. It can be straightforward to combine various losses in a linear fashion. However, hyperparameter fine-tuning and non-convex loss optimization are challenging problems when combining cost functions in such a manner. Here, we propose an efficient formulation of multiple loss components for training GANs. The proposed method, termed HypervolGAN, not only provides an efficient alternative for simultaneous cost optimization, but also boosts model performance in terms of improving generated image quality without excess computation. We further introduce two image-quality-measure based loss components to the GANs specifically for image restoration. Extensive evaluations and results on various benchmark datasets validate the effectiveness of the proposed methods.