  Retinal image quality assessment is an essential task in the diagnosis of
retinal diseases. Recently, there are emerging deep models to grade quality of
retinal images. Current state-of-the-arts either directly transfer
classification networks originally designed for natural images to quality
classification of retinal images or introduce extra image quality priors via
multiple CNN branches or independent CNNs. This paper proposes a dark and
bright channel prior guided deep network for retinal image quality assessment
called GuidedNet. Specifically, the dark and bright channel priors are embedded
into the start layer of network to improve the discriminate ability of deep
features. In addition, we re-annotate a new retinal image quality dataset
called RIQA-RFMiD for further validation. Experimental results on a public
retinal image quality dataset Eye-Quality and our re-annotated dataset
RIQA-RFMiD demonstrate the effectiveness of the proposed GuidedNet.
