Daniela di Serafino, University of Naples Federico II, Napoli, Italy, daniela.diserafino[at]unina.it
Germana Landi, University of Bologna, Bologna, Italy, germana.landi[at]unibo.it
Marco Viola, University of Campania "Luigi Vanvitelli", Caserta, Italy, marco.viola[at]unicampania.it
Version 1.1 - July 18, 2021
ResPoND (Restoration of Poisson-Noisy Directional images) is a MATLAB package for deblurring directional images corrupted by Poisson noise.
The main function in the package, named respond, performs the restoration
by minimizing the KL-DTGV_2 model presented in [1]. Given the noisy and
blurry observed image b, the linear bluring operator A, the background
noise gamma, and the regularization parameter lambda, the function
finds an approximate solution to the nonsmooth constrained minimization
problem
min lambda*KL(A*u + gamma, b) + DTGV_2(u)
s.t. x >= 0,
where KL is the so-called Kullback-Leibler divergence of the blurred image
A*u + gamma from the observed image b and DTGV_2 is the discrete
second-order Total Generalized Variation regularization term. The
minimization problem is solved by a specialized version of a two-block
Alternating Direction Method of Multipliers (ADMM) (see Algorithm 2 in [1]).
The respond function is also suited for the deblurring of general
Poissonian images by the minimization of the KL-TGV_2 model (see the
function documentation for further details).
[1] D. di Serafino, G. Landi and M. Viola, Directional TGV-Based Image Restoration under Poisson Noise, Journal of Imaging, volume 7(6), 2021, p. 99, DOI: 10.3390/jimaging7060099 (open access).
ResPoND runs under MATLAB. It has been tested under MATLAB R2020a.
Here is the list of ResPoND files:
respond.m: main function;dir_est_hough.m: function estimating the main direction of directional images;plot_line_rad.m: function plotting a line along a specified direction at the center of the current image.ssim_index.m: implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images by Zhou Wang.
See the documentation inside each file for further details.
DTGVdemo.m: example of use for the case of directional images;fibre_phantom.mat: phantom directional image;TGVdemo.m: example of use for the case of general images;smooth_phantom.mat: phantom smooth image.
