Open AccessMulti‐frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models
Author(s) -
Yang Afeng,
Jiang Xue,
DayUei Li David
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.4277
Subject(s) - blind deconvolution , deconvolution , maxima and minima , algorithm , recursion (computer science) , noise (video) , computer science , frame (networking) , point spread function , bandwidth (computing) , distortion (music) , computer vision , mathematics , image (mathematics) , telecommunications , mathematical analysis , amplifier
A mixed noise model is proposed and the multi‐frame blind deconvolution is used to restore the images of space objects under the Bayesian inference framework. To minimise the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise.