School of Electrical, Computer and Energy Engineering
PhD Final Oral Defense
Perceptual-Based Locally Adaptive Noise and Blur Detection
by
Tong Zhu
Feb 15, 2016
1:30 PM
GWC 208
Committee:
Dr. Lina Karam (chair)
Dr. Baoxin Li
Dr. Daniel Bliss
Dr. Soe Myint
Abstract
The quality of real-world visual content is typically impaired by many
factors including image noise and blur. Detecting and analyzing these
impairments are important steps for multiple computer vision tasks. This
work focuses on perceptual-based locally adaptive noise and blur
detection. In the context of noise detection, this work proposes a
perceptual-based no-reference objective image noisiness metric by
integrating perceptually weighted local noise into a probability summation
model. The proposed no-reference metric achieves consistently a good
performance across noise types and across databases as compared to
many of the best very recent no-reference quality metrics. In the context of
blur detection, this work proposes a blur detection algorithm that is capable
of detecting and quantifying the level of spatially-varying blur. We compare
our proposed method with six other state-of-the-art blur detection methods.
Experimental results show that the proposed method performs the best
both visually and quantitatively. This work further investigates the
application of the proposed blur detection methods to image deblurring.