Removing Atmospheric Turbulence Effects in Unified Complex Steerable Pyramid Framework

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 unified complex steerable pyramid (CSP) framework. The proposed method first 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 significantenhance visual quality.