/giin

Graph-based Image Inpainting

Primary LanguageMATLABMIT LicenseMIT

GIIN: Graph-based Image Inpainting

Michaël Defferrard. Supervized by Nathanaël Perraudin, Johan Paratte.

The project goal was to explore the applications of spectral graph theory to address the inpainting problem of large missing chunks. We used a non-local patch graph representation of the image and proposed a structure detector which leverages the graph representation and influences the fill-order of our exemplar-based algorithm. Our method achieved state-of-the-art performances.

Full report available at https://infoscience.epfl.ch/record/218020.

inpainting

The algorithm

  1. Identify the potentially fillable unknown pixels.
  2. Give a priority to each of them and identify the one with the highest priority. We will inpaint that pixel.
  3. Find in the image and copy the patch which is the most similar to the patch around the chosen pixel.

Our innovation resides in the way to assign a priority to the unknown pixels. We want to assign a higher priority to the structure than to the texture in order to preserve it while reconstructing. We do it the following way:

  1. Construct a graph of pixels where the edge weights are defined as a similarity measure between the connected pixels.
  2. Place a dirac on the considered pixel.
  3. Diffuse the dirac with a heat kernel. As seen on the image, the diffused signal is constrained by the cluster around the tested pixel. From there, we should be able to determine if the considered pixel is part of a texture or a structure element. Looking at the spreading of the diffusion, texture and structure pixels are easily tell apart. structure diffusion
  4. The priority is then given by the l2 norm of the diffused signal. See the report for the motivation and mathematical definition.
  5. We finally add a confidence priority so that pixels who are farther away from the known pixels get lower priorities. priorities

Installation

Requirements

  • GSPbox: graph signal processing toolbox (version 0.4.0 should work)
  • UNLocBoX: convex optimization toolbox

Optional speed-up

The FLANN library implements fast algorithms to approximate nearest neighbors search. It can be used to speed up the graph construction. It is optional and if you do not want to use it you can comment param.nnparam.use_flann = 1; in lib/giin_patch_graph.m. Otherwise compile it this way:

cd gspbox/3rdparty/sources/flann-1.8.4-src/
mkdir build && cd build
cmake ..
make

Make sure that the MATLAB bin folder is in your path to compile the MEX. If it did work you should have a file called nearest_neighbors.mexa64 in build/src/matlab.

Usage

  1. Place a file image.png in the data sub-folder. The masked area should be bright green, i.e. RGB [0,255,0]. You may want to generate some synthetic images with e.g. giin_image('vertical');.

  2. Launch the inpainting process from MATLAB:

    inpaint('vertical');

    Or from the shell:

    ./launch.sh inpaint vertical

    You may then want to adjust the paths to the toolboxes in launch.sh.