NDLI: ProxImaL: efficient image optimization using proximal algorithms

Content Provider	ACM Digital Library
Author	Heide, Felix Ragan-kelley, Jonathan Wetzstein, Gordon Diamond, Steven Nießner, Matthias Heidrich, Wolfgang
Copyright Year	2016
Description	Author Affiliation: KAUST and University of British Columbia(Stanford university and university of british columbia (heide, Felix; Wetzstein, Gordon); Ragan-kelley, Jonathan; Niener, Matthias; Stanford university (diamond, Steven; Heidrich, Wolfgang))
Abstract	Computational photography systems are becoming increasingly diverse, while computational resources---for example on mobile platforms---are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	07300301
e-ISSN	15577368
DOI	10.1145/2897824.2925875
Volume Number	35
Issue Number	4
Journal	ACM Transactions on Graphics (TOG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational photography Digital image processing Optimization
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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