title: A CNN based neurobiology inspired approach for retinal image quality assessment.
abstract: Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods.
authors.forename: Dwarikanath
authors.initials: D
authors.lastname: Mahapatra
authors.forename: Pallab K
authors.initials: PK
authors.lastname: Roy
authors.forename: Suman
authors.initials: S
authors.lastname: Sedai
authors.forename: Rahil
authors.initials: R
authors.lastname: Garnavi
mesh_terms: D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D009446:Neurobiology; D012160:Retina