Total-Variation

Total variation image denosing and deblurring.

Python implementation of Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems by Amir Beck and Amir Teboulle (http://www.math.tau.ac.il/~teboulle/papers/tlv.pdf). Link for code on paper no longer works; use the TV_FISTA download from https://sites.google.com/site/amirbeck314/software (this link also has another download for the paper).

Generated the two mat files by following these two parameter sets from guide_tv_deblur.pdf (included in the code download linked above):

• cameraman_Bobs.mat:

  X = double(imread('cameraman.pgm'));
  X = X/255;
  randn('seed',314);
  Bobs=X+2e-2*randn(size(X))

  Used for denoising task.
• cameraman_Bobs_blurry.mat:

  X = double(imread('cameraman.pgm'));
  X = X/255;
  P=1/9*ones(3,3);
  center=[2,2];
  randn(’seed’,314);
  Bobs=imfilter(X,P,’symmetric’)+1e-4*randn(size(X));

  Used for deblurring/denoising task. Note: Due to difference in indexing between Matlab and Python, the center in Python will be center - [1 1]. So, in the above example, in Python set center to [1 1]

environment.yaml contains the yaml file to create the conda environment called total-var. Run conda env create -f environment.yaml.