This is an implementation of a forward and reverse computational photography pipeline
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README.md

README.md

ReversiblePipeline

This is an implementation of a pre-learned camera image processing model. A description of the model can be found in "A New In-Camera Imaging Model for Color Computer Vision and its Application" by Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Susstrunk, Stephen Lin, and Michael S. Brown. Code for learning a new model can be found at the original project page. This particular implementation was written by Mark Buckler.

If you are looking for the full Configurable & Reversible Imaging Pipeline (CRIP) as seen in the paper Reconfiguring the Imaging Pipeline for Computer Vision by Mark Buckler, Suren Jayasuriya, and Adrian Sampson, see this repository instead.

Original Project Page:

Model Format Readme:

Pipeline Implementation Descriptions

  • Strict Forward Pipeline

A forward only pipeline which displays intermediate as well as final outputs is provided in the src/scripts directory. Use this if you want a fast forward pipeline but dont need to emulate a specific camera model or reverse the processing. This implementation uses LibRaw and OpenCV.

  • Reversible Matlab Pipeline

This implementation can be found in src/Matlab. Use this pipeline if you would like to use one of the available forward and backward camera pipeline models, aren't concerned about speed, and would like to take advantage of Matlab's many pre-built image processing tools. Note that this implementation is prohibitively slow if you would like to process large images. Its default is to do patch based processing to enable a reasonable run time.

  • Reversible Halide Pipeline

This can be found in src/Halide. Use this pipeline if you want to process full high resolution images with both a backward and forward image processing pipeline implementation. The model is fully implemented with Halide Funcs, and some basic scheduling for loop unrolling and pre-computation is provided. Scheduling for paralellism on the CPU or GPU can be easily added by the user. Do note that while this implementation is much faster than Matlab, it will take close to 10 minutes to process a 5000x3000 pixel image.

General Use Instructions

Instructions for using all of the different pieces of the pipeline can be found in readmes like this one within the appropriate directories. All code has been tested with Ubuntu 14.04, but with small edits to makefiles all code should work with OS X as well.

Dependencies

  • Development versions of LibRaw and OpenCV (for preprocessing)
  • Matlab (for Matlab pipeline)
  • Halide (for Halide pipeline)