ProxImaL: efficient image optimization using proximal algorithms

Felix Heide, Steven Diamond, Matthias Nießner, Jonathan Ragan-Kelley, Wolfgang Heidrich, Gordon Wetzstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationACM Transactions on Graphics
PublisherAssociation for Computing Machinery (ACM)
Number of pages15
StatePublished - Jul 11 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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