Perceptually-calibrated synergy network for night-time image quality assessment with enhancement booster and knowledge cross-sharing

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Image quality assessment (IQA) and image enhancement (IE) of night-time images are highly correlated tasks. On the one hand, IQA task could obtain more complementary information from the enhanced image. On the other hand, IE task would benefit from the prior knowledge of quality-aware attributes. Thus, we propose a Perceptually-calibrated Synergy Network (PCSNet) to simultaneously predict and enhance image quality of night-time images. More specifically, a shared shallow network is applied to extract the shared features for both tasks by leveraging complementary in-formation. The shared features are then fed to task-specific sub-networks to predict quality scores and generate enhanced images in parallel. In order to better exploit the interaction of complementary information, intermediate Cross-Sharing Modules are used to form efficient feature representations for the image quality assessment (IQA) and image enhancement (IE) subnetworks. Experimental results of the night-time image datasets show that the proposed approach achieves state-of-the-art performance on both quality prediction and image enhancement tasks.