Abstract:
Simultaneously removing atmospheric turbulence-induced geometric distortion and blurry
degradation is a challenging task. In this paper, we propose an effective method to remove or at least
reduce turbulence effects in uni ed complex steerable pyramid (CSP) framework. The proposed method rst
decomposes the degraded image sequence by CSP. Then, the local motion and the energy information of the
image sequence can be represented by multiscale and multidirectional phases and amplitudes. To mitigate
turbulence-induced random oscillation, we use temporal average phase as the initial reference phase. Then,
the reference phase is iteratively corrected, using the proposed phase correction method which is capable
of correcting the large displacement. To reduce blurry degradation, optimal amplitude selection and fusion
methods are proposed to reduce blur variation and CSP reconstruction errors. Finally, the corrected phase
and fused amplitude can be synthesized to generate a reconstructed image. To further enhance the image
quality, a blind deconvolution approach is adopted to deblur the reconstructed image. Through a variety
of experiments on the simulated and real data, experimental results show that the proposed method can
effectively alleviate the turbulence effects, recover image details, and signi cantenhance visual quality.