Tear Removal

Research.TearRemoval History

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Figure 2: The segmented frame. The colours (red/green) represent the region to which each pixel is assigned
Figure 3: The segmented frame with displacement removed. Edges across tear are aligned.
Figure 4: The restored frame after damaged data is recovered.
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Figure 1: A torn frame
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keywords: Tear Removal, Motion Estimation, Digital Movie Restoration
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keywords: Tear Removal, Motion Estimation, Image Segmentation, Digital Movie Restoration
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Examples

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  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude [1].
  2. Division of torn frames into regions divided by the tear boundary - [2].
  3. Estimation of and compensation for the relative displacement between the regions - [1].
  4. Recovery of damaged image data - JOMBANDI [3].
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  1. Detection of any torn frames in a sequence [1].
  2. Division of torn frames into regions divided by the tear boundary [2].
  3. Estimation of and compensation for the relative displacement between the regions [1].
  4. Recovery of damaged image data [3].
September 06, 2007 by 134.226.86.54 -
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author: David Corrigan
keywords: Tear Removal, Motion Estimation, Digital Movie Restoration
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1. Automatic Treatment of Film Tear in Degraded Archived Media. David Corrigan and Anil Kokaram (2004) In ICIP '04. Singapore, October.

2. Automated Segmentation of Torn Frames using the Graph Cuts Technique. David Corrigan , Naomi Harte and Anil Kokaram (2007) In IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, USA, September. ( pdf )

3. MCMC for joint noise reduction and missing data treatement in Degraded Video. A. Kokaram and S. J. Godsill (2002) IEEE Transactions on Signal Processing, Special Issue on MCMC, 50(2):189-205.

to:
  1. Automatic Treatment of Film Tear in Degraded Archived Media. David Corrigan and Anil Kokaram (2004) In ICIP '04. Singapore, October.
  2. Automated Segmentation of Torn Frames using the Graph Cuts Technique. David Corrigan, Naomi Harte and Anil Kokaram (2007) In IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, USA, September. ( pdf )
  3. MCMC for joint noise reduction and missing data treatement in Degraded Video. A. Kokaram and S. J. Godsill (2002) IEEE Transactions on Signal Processing, Special Issue on MCMC, 50(2):189-205.
September 06, 2007 by 134.226.86.54 -
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  1. Division of torn frames into regions divided by the tear boundary -
  2. Estimation of and compensation for the relative displacement between the regions
  3. Recovery of damaged image data
to:
  1. Division of torn frames into regions divided by the tear boundary - [2].
  2. Estimation of and compensation for the relative displacement between the regions - [1].
  3. Recovery of damaged image data - JOMBANDI [3].
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Automatic Treatment of Film Tear in Degraded Archived Media. David Corrigan and Anil Kokaram (2004) In ICIP '04. Singapore, October.

Automated Segmentation of Torn Frames using the Graph Cuts Technique. David Corrigan , Naomi Harte and Anil Kokaram (2007) In IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, USA, September. ( pdf )

to:

1. Automatic Treatment of Film Tear in Degraded Archived Media. David Corrigan and Anil Kokaram (2004) In ICIP '04. Singapore, October.

2. Automated Segmentation of Torn Frames using the Graph Cuts Technique. David Corrigan , Naomi Harte and Anil Kokaram (2007) In IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, USA, September. ( pdf )

3. MCMC for joint noise reduction and missing data treatement in Degraded Video. A. Kokaram and S. J. Godsill (2002) IEEE Transactions on Signal Processing, Special Issue on MCMC, 50(2):189-205.

September 06, 2007 by 134.226.86.54 -
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  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude [1].
  2. Division of torn frames into regions divided by the tear boundary - The most recent algorithm published by the group (insert reference here) takes inspiration from the iterative segmentation technique outlined by Boykov (insert reference here).
to:
  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude [1].
  2. Division of torn frames into regions divided by the tear boundary -
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  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude.
to:
  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude [1].
September 06, 2007 by 134.226.86.54 -
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  1. Detection of any torn frames in a sequence - To date, this problem has been approached by examining the gradient content of each frame (insert reference). As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude.
to:
  1. Detection of any torn frames in a sequence - As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude.
September 06, 2007 by 134.226.86.54 -
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Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the restoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

to:

Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the restoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

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Bibliography

Automatic Treatment of Film Tear in Degraded Archived Media. David Corrigan and Anil Kokaram (2004) In ICIP '04. Singapore, October.

Automated Segmentation of Torn Frames using the Graph Cuts Technique. David Corrigan , Naomi Harte and Anil Kokaram (2007) In IEEE International Conference on Image Processing (ICIP). San Antonio, Texas, USA, September. ( pdf )

September 06, 2007 by 134.226.86.54 -
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  1. Detection of any torn frames in a sequence
  2. Division of torn frames into regions divided by the tear boundary
to:
  1. Detection of any torn frames in a sequence - To date, this problem has been approached by examining the gradient content of each frame (insert reference). As tears are significant edge features, there are significant regions of high gradient magnitude. Torn frames may be detected by searching for frames with abnormally high levels of pixels with a large gradient magnitude.
  2. Division of torn frames into regions divided by the tear boundary - The most recent algorithm published by the group (insert reference here) takes inspiration from the iterative segmentation technique outlined by Boykov (insert reference here).
September 06, 2007 by 134.226.86.54 -
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  1. Detecting any torn frames in a sequence
  2. Dividing torn frames into two regions divided by the tear boundary
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Tear Removal must address the following challenges

  1. Detection of any torn frames in a sequence
  2. Division of torn frames into regions divided by the tear boundary
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  1. Recovery of damaged image data
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  1. Recovery of damaged image data
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  1. Detecting any torn frames in a sequence
  2. Dividing torn frames into two regions divided by the tear boundary
  3. Estimation of and compensation for the relative displacement between the regions
  4. Recovery of damaged image data
September 05, 2007 by 134.226.86.54 -
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Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the resoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

The steps involved in the tear rstoration are as follows

\begin{enumerate}

  \item yeah
  \item yeo

\end{enumerate}

to:

Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the restoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

September 05, 2007 by 134.226.86.54 -
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Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the resoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

to:

Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the resoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

The steps involved in the tear rstoration are as follows

\begin{enumerate}

  \item yeah
  \item yeo

\end{enumerate}

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tytyu

to:

Tear Removal is concerned with the restoration of sequences in which the film material has been physically torn. In torn frames, two issues need to be addressed. Firstly, the regions divided by the tear need to be re-aligned. This can be achieved by estimating the motion of the two regions. Once the regions have been aligned correctly, the second stage of the resoration is to recover the damage image data along the tear boundary. A solution is found by treating it as a missing data problem. The data is interpolated from the appropriate regions in neighbouring frames.

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September 05, 2007 by 134.226.86.54 -
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tytyu

Page last modified on September 13, 2007