1 research outputs found
Optimization of Structural Similarity in Mathematical Imaging
It is now generally accepted that Euclidean-based metrics may not always
adequately represent the subjective judgement of a human observer. As a result,
many image processing methodologies have been recently extended to take
advantage of alternative visual quality measures, the most prominent of which
is the Structural Similarity Index Measure (SSIM). The superiority of the
latter over Euclidean-based metrics have been demonstrated in several studies.
However, being focused on specific applications, the findings of such studies
often lack generality which, if otherwise acknowledged, could have provided a
useful guidance for further development of SSIM-based image processing
algorithms. Accordingly, instead of focusing on a particular image processing
task, in this paper, we introduce a general framework that encompasses a wide
range of imaging applications in which the SSIM can be employed as a fidelity
measure. Subsequently, we show how the framework can be used to cast some
standard as well as original imaging tasks into optimization problems, followed
by a discussion of a number of novel numerical strategies for their solution