TY - JOUR
T1 - Progress in the restoration of image sequences degraded by atmospheric turbulence
AU - Gal, Ronen
AU - Kiryati, Nahum
AU - Sochen, Nir
PY - 2014/10/15
Y1 - 2014/10/15
N2 - In principle, a still image can be reconstructed from a turbulent video of a static scene using the classic 'sum and deblur' approach (with or without a preliminary registration step). However, the performance of such turbulence recovery algorithms has so far been limited. In the last decade, significant progress has been achieved in non-rigid registration and in image deblurring. We revisit the turbulence recovery problem, incorporating state of the art registration and deblurring algorithms as building blocks within the sum and deblur framework. Accurate pre-registration of the input video frames narrows the spatial support of the effective blur kernel affecting the sum of the turbulent video sequence. Powerful registration is therefore crucial for successful reconstruction. We employ a two-phase registration process, consisting of rigid registration followed by non-rigid refinement. For rigid registration, we adopt the recent algorithm of Lazaridis and Petrou (2006) [1]. Using real turbulent video data, we demonstrate excellent turbulence recovery.
AB - In principle, a still image can be reconstructed from a turbulent video of a static scene using the classic 'sum and deblur' approach (with or without a preliminary registration step). However, the performance of such turbulence recovery algorithms has so far been limited. In the last decade, significant progress has been achieved in non-rigid registration and in image deblurring. We revisit the turbulence recovery problem, incorporating state of the art registration and deblurring algorithms as building blocks within the sum and deblur framework. Accurate pre-registration of the input video frames narrows the spatial support of the effective blur kernel affecting the sum of the turbulent video sequence. Powerful registration is therefore crucial for successful reconstruction. We employ a two-phase registration process, consisting of rigid registration followed by non-rigid refinement. For rigid registration, we adopt the recent algorithm of Lazaridis and Petrou (2006) [1]. Using real turbulent video data, we demonstrate excellent turbulence recovery.
KW - Atmospheric turbulence
KW - Deconvolution
KW - Non-rigid registration
KW - Shift and add
KW - Sum and deblur
KW - Video restoration
UR - http://www.scopus.com/inward/record.url?scp=84906785917&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2014.04.007
DO - 10.1016/j.patrec.2014.04.007
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AN - SCOPUS:84906785917
SN - 0167-8655
VL - 48
SP - 8
EP - 14
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
ER -