Abstract
Predicting the saliency of images affected by distortion is a challenging but emerging research problem. Given a distorted image, we wish to accurately predict saliency as perceived by humans. A recent distortion-aware saliency benchmark – the CUDAS database – reveals the inadequacy of existing saliency models in handling distorted images. In this paper, we devise a deep learning Distortion-Aware Saliency Module (DASM) that enables capturing saliency features related to image distortions, and integrates this module into a saliency prediction architecture. To achieve the high expressive capability of DASM using supervised learning, we create a dedicated dataset that draws upon a large-scale saliency dataset and machine-generated image quality assessments. Experimental results demonstrate the superior performance of the proposed model in predicting the saliency of distorted images.
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