Fried deconvolution
This work addresses image quality issues for long-range imaging applications, but it is incremental as it builds on existing deconvolution methods with a specific kernel.
The paper tackles the problem of deblurring atmospheric turbulence in long-range imaging by using the Fried kernel for the modulation transfer function and a framelet-based deconvolution algorithm, achieving very good results on simulated and real images.
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.