DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks

Understand

Synthesizing high quality saliency maps from noisy images is a challenging problem in computer vision and has many practical applications.

• Samples generated by existing techniques for saliency detection cannot handle the noise perturbations smoothly and fail to delineate the salient objects present in the given scene.
• In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images.
• DSAL-GAN consists of two generative adversarial-networks (GAN) trained end-to-end to perform denoising and saliency prediction altogether in a holistic manner.