[IPOL announce] new article: Estimating an Image's Blur Kernel Using Natural Image Statistics, and Deblurring it: An Analysis of the Goldstein-Fattal Method

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2018/211/

Jérémy Anger, Gabriele Facciolo, and Mauricio Delbracio,
Estimating an Image's Blur Kernel Using Natural Image Statistics, and 
Deblurring it: An Analysis of the Goldstein-Fattal Method,
Image Processing On Line, 8 (2018), pp. 282–304.
https://doi.org/10.5201/ipol.2018.211

Abstract
Despite the significant improvement in image quality resulting from 
improvement in optical sensors and general electronics, camera shake 
blur significantly undermines the quality of hand-held photographs. In 
this work, we present a detailed description and implementation of the 
blur kernel estimation algorithm introduced by Goldstein and Fattal in 
2012. Unlike most methods that attempt to solve an inverse problem 
through a variational formulation (e.g. through a Maximum A Posteriori 
estimation), this method directly estimates the blur kernel by modeling 
statistical irregularities in the power spectrum of blurred natural 
images. The adopted mathematical model extends the well-known power-law 
by contemplating the presence of dominant strong edges in particular 
directions. The blur kernel is retrieved from an estimation of its power 
spectrum, by solving a phase retrieval problem using additional 
constraints associated with the particular nature of camera shake blur 
kernels (e.g. non-negativity and small spatial support). Although the 
algorithm is conceptually simple, its numerical implementation presents 
several challenges. This work contributes to a detailed anatomy of the 
Goldstein and Fattal method, its algorithmic description, and its 
parameters.