Publication: Image processing and knowledge-based techniques for automated quality improvement of digital images
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2017
Defense date
2017-09-27
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Abstract
With its multidisciplinary nature drawing upon a great variety of areas such
as mathematics, computer graphics, computer vision, visual psychophysics,
optics, and computer science, the theory of Image Processing needs to be made
accessible to practitioners from very diverse backgrounds, from amateur
photographers to specialists in communications, medicine, or biology.
The presented thesis focusses on improving quality of images as an automatic
process, where we regard aesthetic, physical, perceptual and cognitive
approaches (which respectively result in pleasant, identical, realistic, and
detailed images) as different levels of the same problem: reproduction accuracy.
Main contributions include self-contained fundamental material whose value is
likely to remain applicable in a rapidly evolving body of knowledge. A basic
strategy followed in its preparation was to provide a seamless integration of wellestablished
theoretical concepts and their implementation using state-of-the-art
software tools.
We divide the problem into i) low-level algorithmic routines, which resemble
early-vision stages, where prior knowledge about natural images is implicitly
‘coded’ in the algorithm itself; and ii) explicit knowledge representation for highlevel
image processing tasks involving algorithm composition, execution,
revision and comparison of candidate solutions.
First, we perform a deep theoretical research of classical as well as emerging
paradigms in image quality and its two main dimensions, noise and tone
reproduction, with emphasis on information theory and signal processing, but
also with inspiration from perceptual sciences and computational photography,
thus providing a unifying approach. We focus on edge-preserving smoothing
filters as a simple, yet very powerful low-level image processing tool to deal with
noise reduction and improved tone reproduction by means of extracting intrinsic
components of an image. Finally, we develop, prototype and execute selected
low-level image processing operators in MATLAB.
Second, in order to enable end-users to accomplish complex Image Processing
tasks while at the same time limiting their cognitive and skill requirements, a
system is provided in which expert’s knowledge is explicitly stated in the form
of rules. Developed with classical knowledge-based techniques and finally
implemented in Java, the proposed system allows easy adaptation to specific
tasks by exchanging knowledge bases for different areas like computer vision,
remote sensing or medical image analysis.
Last, but not least, we are not only interested in solving Image Processing
problems, we also want to capture, understand and share the reasoning behind
so that others, including non-expert users, can use and build on it.
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Keywords
Image quality improvement, Digital image processing, Mathematical analysis, Computer programming, Visual perception