CNN-GRNN for Image Sharpness Assessment

Image sharpness is key to readability and scene understanding. Because of the inaccessible reference information, blind image sharpness assessment (BISA) is useful and challenging. In this paper, a shallow convolutional neural network (CNN) is proposed for intrinsic representation of image sharpness and general regression neural network (GRNN) is utilized for precise score prediction. The hybrid CNN-GRNN model tends to build functional relationship between retrieved features and subjective human scores by supervised learning. Superior to traditional algorithms based on handcrafted features and machine learning, CNN-GRNN fuses feature extraction and score prediction into an optimization procedure. Experiments on Gaussian blurring images in LIVE, CSIQ, TID2008 and TID2013 show that CNN-GRNN outperforms the state-of-the-art algorithms and gets closer to human subjective judgment.