The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric
turbulence. Image deconvolution such as iterative blind deconvolution (IBD) and Richardson-Lucy (RL) deconvolution
are required. The IBD method involves the imposition of constraints such as conservation of energy, positivity, and finite
support, with known size, alternately on the image and the PSF in the spatial and Fourier domains, until convergence.
The iterative RL solution converges to the maximum likelihood solution for Poisson statistics in the data. Properties of
the RL algorithm which make it well-suited for IBD are energy conservation and the sustenance of nonnegativity. So, RL
was incorporated into the IBD framework. In this paper, an enhanced Richardson-Lucy-based iterative blind
deconvolution (ERL-IBD) algorithm is proposed to restore the blurred images due to atmospheric turbulence. The ERLIBD
incorporates dynamic PSF support estimation, bandwidth constraint of optical system, and the asymmetry factor
update. The experimental results demonstrate that the ERL-IBD algorithm works better than IBD algorithm in
deconvolution of the blurred-turbulence image.