Image Sharpness Metric Based on Maxpol Convolution Kernels

Abstract

We presents a no-reference (NR) image sharpness metric based on a visual sensitivity model. We propose that MaxPol convolution kernels are close approximation to this model and capable of extracting meaningful features for image sharpness assessment. Equipped by these kernels, we develop an efficient pipeline to evaluate the out-of-focus level of input images by decomposing the first and third order image differentials. The associated kernels are regulated in higher cutoff frequencies to balance out the information loss and noise sensitivity. We use high order central moments to exploit sharpness scores in wide range of frequency information. The experimental results outperform the state-of-the-art methods in accuracy and speed.