MATLAB PROJECT
Encoding Visual
Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment
Abstract:
In this paper, we propose a novel design of human visual system (HVS)
response in a convolutional filter form to decompose meaningful features that
are closely tied with image sharpness level. No-reference (NR) image sharpness
assessment (ISA) techniques have emerged as the standard of image quality
assessment in diverse imaging applications. Despite their high correlation with
subjective scoring, they are challenging for practical considerations due to
high computational cost and lack of scalability across different image blurs.
We bridge this gap by synthesizing the HVS response as a linear combination of
finite impulse response derivative filters to boost the falloff of high band
frequency magnitudes in natural imaging paradigm. The numerical implementation
of the HVS filter is carried out with MaxPol filter library that can be
arbitrarily set for any differential orders and cutoff frequencies to balance
out the estimation of informative features and noise sensitivities. Utilized by
the HVS filter, we then design an innovative NR-ISA metric called “HVS-MaxPol”
that 1) requires minimal computational cost, 2) produces high correlation
accuracy with image sharpness level, and 3) scales to assess the synthetic and
natural image blur. Specifically, the synthetic blur images are constructed by
blurring the raw images using a Gaussian filter, while natural blur is observed
from real-life application such as motion, out-of-focus, and luminance
contrast. Furthermore, we create a natural benchmark database in digital
pathology for validation of image focus quality in whole slide imaging systems
called “FocusPath” consisting of 864 blurred images. Thorough experiments are
designed to test and validate the efficiency of HVS-MaxPol across different
blur databases and the state-of-the-art NR-ISA metrics. The experiment result
indicates that our metric has the best overall performance with respect to
speed, accuracy, and scalability.
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