Fried deconvolution

TL;DR

A new approach to deblur the effect of atmospheric turbulence in the case of long range imaging is presented, based on an analytical formulation, the Fried kernel, of the atmosphere modulation transfer function and a framelet based deconvolution algorithm.

Abstract

In this paper we present a new approach to deblur the effect of atmospheric turbulence in the case of long range imaging. Our method is based on an analytical formulation, the Fried kernel, of the atmosphere modulation transfer function (MTF) and a framelet based deconvolution algorithm. An important parameter is the refractive index structure which requires specific measurements to be known. Then we propose a method which provides a good estimation of this parameter from the input blurred image. The final algorithms are very easy to implement and show very good results on both simulated blur and real images.

Figures (13)

• Figure 1: Examples of Fried kernel (see text for parameter's values).
• Figure 2: Examples of an original and its corresponding blurred images.
• Figure 3: Blurred and nonblind Fried deconvolved images (first column: $C_n^2=7\times 10^{-14}m^{-2/3}$, second column: $C_n^2=2\times 10^{-13}m^{-2/3}$, third column: $C_n^2=5\times 10^{-13}m^{-2/3}$).
• Figure 4: Nonblind Fried deconvolution on real barchart. Original images are on top, deconvolved ones on bottom. Original acquired image is used on first column and a geometric corrected one on the second column (see text for more explanations).
• Figure 5: Nonblind Fried deconvolution on real letter board. Original images are on top, deconvolved ones on bottom. Original acquired image is used on first column and a geometric corrected one on the second column (see text for more explanations).