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Compressed sensing based solutions for image inpainting and super-resolution that use Gaussian Mixture Models, implemented as part of a course project for CS 754 - Advanced Image Processing.

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CS-GMM

This repository contains our implementation and report on compressed sensing based solutions for image inpainting and interpolative zooming using Gaussian Mixture Models, completed for a course project in CS 754 - Advanced Image Processing, in Spring 2021.

This work is based on the following paper: https://arxiv.org/pdf/1006.3056.pdf.

Authors: Ankit Kumar Misra (190050020) and Dhruva Dhingra (190070020).

Directory structure:

  • report.pdf is a report on the mathematics behind this method and the performance results of our implementations.
  • code contains MATLAB programs that implement image inpainting and interpolative zooming techniques using Gaussian Mixture Models. It also contains code for a special initialization of the GMM parameters, as per the paper linked above.
  • data contains the three images on which our implementations were tested to analyze performance.
  • results contains inpainted and zoomed image results generated by our programs, for all three images in data.

When running the MATLAB programs, do make sure they are present in the same directory, since some of the files are inter-dependent.

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Compressed sensing based solutions for image inpainting and super-resolution that use Gaussian Mixture Models, implemented as part of a course project for CS 754 - Advanced Image Processing.

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