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Welcome to ProxImaL — ProxImaL 0.1.3 documentation

ProxImaL is a Python-embedded modeling language for image optimization problems. It allows you to express your problem in a natural way that follows the math, and automatically determines an efficient method for solving the problem. ProxImaL makes it easy to experiment with many different priors and other problem reformulations, without worrying about the details of how the problem is solved. For example, the following code denoises an image using simple sparse gradient and nonnegativity priors:.

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Welcome to ProxImaL — ProxImaL 0.1.3 documentation | proximal-lang.org Reviews

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ProxImaL is a Python-embedded modeling language for image optimization problems. It allows you to express your problem in a natural way that follows the math, and automatically determines an efficient method for solving the problem. ProxImaL makes it easy to experiment with many different priors and other problem reformulations, without worrying about the details of how the problem is solved. For example, the following code denoises an image using simple sparse gradient and nonnegativity priors:.

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Welcome to ProxImaL — ProxImaL 0.1.3 documentation | proximal-lang.org Reviews

https://proximal-lang.org

ProxImaL is a Python-embedded modeling language for image optimization problems. It allows you to express your problem in a natural way that follows the math, and automatically determines an efficient method for solving the problem. ProxImaL makes it easy to experiment with many different priors and other problem reformulations, without worrying about the details of how the problem is solved. For example, the following code denoises an image using simple sparse gradient and nonnegativity priors:.

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proximal-lang.org

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Publications — ProxImaL 0.1.3 documentation

http://www.proximal-lang.org/en/latest/publications/index.html

The following publications discuss the ideas behind ProxImaL and show how ProxImaL can be used to obtain state-of-the-art results in computational photography. ProxImaL: Efficient Image Optimization Using Proximal Algorithms. F Heide, S. Diamond, M. Niessner, J. Ragan-Kelley, W. Heidrich, and G. Wetzstein. Proceedings of ACM SIGGRAPH, 2016. Enter search terms or a module, class or function name. 2016, Felix Heide and Steven Diamond. Powered by Sphinx 1.3.5. Alabaster 0.7.9.

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Install Guide — ProxImaL 0.1.3 documentation

http://www.proximal-lang.org/en/latest/install/index.html

The easiest way to install ProxImaL and its dependencies is using Anaconda. To install ProxImaL without Anaconda. See the section on installation from source. On Linux and Mac OS X, install all Python dependencies by running. Conda install numpy scipy pil pip opencv. On Windows, install all Python dependencies by running. Conda install numpy scipy pil pip conda install -c https:/ conda.binstar.org/menpo opencv. Environment variable to point to the installation. Also, add the Halide. For Mac OS X.

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Tutorial — ProxImaL 0.1.3 documentation

http://www.proximal-lang.org/en/latest/tutorial/index.html

In this tutorial we cover the linear operators, proxable functions, and solver algorithms available. Creates a variable object. Element-wise multiplication with a fixed constant weight array. Sums list of input expressions into a single linear expression. Vectorizes and stacks a list of input expressions into a single linear expression. Computes the gradients of. By default across all of its dimensions. As a 2D image and warps it using the homography. Or the predefined opponent (. The squared L2 norm.

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computationalimaging.org

Computational Imaging Publications

http://www.computationalimaging.org/publications

ProxImaL: Efficient Image Optimization using Proximal Algorithms SIGGRAPH 2016. ProxImaL is a Python-embedded modeling language for image optimization problems. Multi-Camera Time-of-Flight Systems SIGGRAPH 2016. Computational imaging with multi-camera time-of-flight imaging systems. Focus-tunable and Monovision Near-eye Displays SIGCHI 2016. Variable Aperture Light Field Photography: Overcoming the Diffraction-limited Spatio-angular Resolution Tradeoff CVPR 2016. The Light Field Stereoscope SIGGRAPH 2015.

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Welcome to ProxImaL — ProxImaL 0.1.3 documentation

ProxImaL is a Python-embedded modeling language for image optimization problems. It allows you to express your problem in a natural way that follows the math, and automatically determines an efficient method for solving the problem. ProxImaL makes it easy to experiment with many different priors and other problem reformulations, without worrying about the details of how the problem is solved. For example, the following code denoises an image using simple sparse gradient and nonnegativity priors:.

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