Handling Transparency in Digital Video


Automated blotch removal is important in film restoration and typically involves a detection/interpolation step. Current algorithms model the corruption as a binary mixture between the original, clean images and an opaque (dirt) field. This typically causes incomplete blotch removal that manifests as blotch haloes in reconstruction. This paper proposes a new approach by modeling the corruption as a continuous mixture between the two components and generating a solution using a Bayesian framework. We use novel priors, propose a computationally efficient scheme for implementation and our results show more complete blotch reconstruction.

Figure: Corrupted Image Sequence
Figure: Restoration using NUKE from
Figure: Restoration using our approach

Reflection Detection

Reflections in image sequences consist of several layers superimposed over each other. This phenomenon causes many image processing techniques to fail as they assume the presence of only one layer at each examined site e.g. motion estimation and object recognition. This work presents an automated technique for detecting reflections in image sequences by analyzing motion trajectories of feature points. It models reflection as regions containing two different layers moving over each other. We present a strong detector based on combining a set of weak detectors. We use novel priors, generate sparse and dense detection maps and our results show high detection rate with rejection to pathological motion and occlusion.

Figure: Reflection/Transparency detection in image sequences. Our technique is also compared against two approaches DFD and Sharpness.

Multiple Motion Estimation

Regions of reflections contain two semi-transparent layers moving over each other. This generates two motion vectors per pel. Current multiple motion estimators either extend the usual brightness consistency assumption to two motions or are based on the Fourier phase shift relationship. Both approaches assume constant motion over at least three frames. As a result they can not handle temporally active motion due to camera shake or acceleration. This paper proposes a new approach for multiple motion estimation by modeling the correct motions as the ones generating the best layer separation of the examined reflection. A Bayesian framework is proposed which then admits a solution using candidate motions generated from KLT trajectories and a layer separation technique. We use novel temporal priors and our results show handling of strong motion inconsistencies and improvements over previous work.

Figure: Multiple motion estimation for regions of reflections. Our technique (BIMS) is compared against two approaches FTRANS and OPTIC.
Page last modified on September 09, 2011