Restoration of Old Films Using Morphological Operators · Restoration of Old Films Using...
Transcript of Restoration of Old Films Using Morphological Operators · Restoration of Old Films Using...
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Restoration of Old Films Using MorphologicalOperators
Quôc Peyrot [email protected]
LRDE seminar, September 25, 2002
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Table des matières
Table des matières
Introduction .......................................................................................... 2
History background ............................................................................. 3
Mathematical morphology .................................................................. 4
Connexity............................................................................................ 6
Operators............................................................................................ 8
Restoration of vertical scratches ....................................................... 18
Restoration of local random defects ................................................. 31
Conclusion ........................................................................................... 47
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Introduction
Introduction
• Invention of “Lumière Cinematograph” in 1895 by Lumière brothers
• Deterioration due to short life span of films used
. It is urgent to restore and preserve this cultural inheritance.
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History background
History background
• Classic way : physic and chemical restorations
. Time-consuming and money-consuming
European Commission created two projects:
• AURORA Project: AUtomated Restoration of ORiginal film and videoArchives(Ended in 1999)
• Brava Project: BRoadcast Archives Restoration Through VideoAnalyses(Feb 2000 - Nov 2002)
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Mathematical morphology
Mathematical morphology
• Instantiated by G. Matheron and J. Serra in 1964
. Study geometric structures
• Etienne Decencière Ferrandière, Restauration automatique de filmsanciens, PhD, 1997
. Bases of this study
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Mathematical morphology
Implementation
• Language: C++
• Image processing and Mathematical morphology provided by Olena :a generic image processing library in C++ from EPITA Research andDevelopment Laboratory (LRDE1)
1http://www.lrde.epita.fr
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Mathematical morphology Connexity
Connexity
Common connexity:
• 4-connexity
• 8-connexity
• 6-connexity (for hexagonal)
. we can have 3d-connexity and time connexity !
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Mathematical morphology Connexity
Connexity sample
4−Connexity 6−Connexity 8−Connexity
Figure 1: Connexity
window2d win;win.add(0,0).add(1,0).add(0,1).add(-1,0).add(0,-1);
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Mathematical morphology Operators
Operators
• Primitives : Dilation and erosion
• Closing - Opening
• Reconstruction
• Area filter
• White Top-hat / Black Top-hat
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Mathematical morphology Operators
Image sample
Figure 2: Image sample
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Mathematical morphology Operators
Dilation - Erosion
Figure 3: Dilation - Erosion
• Dilation : δS(A)
• Erode : εS(A)
image2d<int_u8> result = morpho::dilation(lena, win_c8p());image2d<int_u8> result = morpho::erosion(lena, win_c8p());
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Mathematical morphology Operators
Closing - Opening
Figure 4: Closing - Opening
• Closing : γS(A) = (εS ◦ δS)(A)
• Opening : φS(A) = (δS ◦ εS)(A)
image2d<int_u8> result = morpho::closing(lena, win_c8p());image2d<int_u8> result = morpho::opening(lena, win_c8p());
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Mathematical morphology Operators
Reconstruction
Figure 5: Reconstruction : mark and result
morpho::sure_geodesic_reconstruction_dilation(marker,mask,neighb_c8());
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Mathematical morphology Operators
Area filters
Figure 6: Minima killing with area=9
level::fast_minima_killer(image, area, neighb_c8());
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Mathematical morphology Operators
White Tophat - Black Tophat
Figure 7: White Tophat - Black Tophat
• White Tophat : wthS(I) = I − γS(I)
• Black Tophat : bthS(I) = φS(I)− I
morpho::white_top_hat(lena, win_c8p());morpho::black_top_hat(lena, win_c8p());
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Mathematical morphology Operators
Gray images
•
I =G−1∑g=1
Thresholdg(I)
•
op(I) =G−1∑g=1
op(Thresholdg(I))
. This is slow : there are other ways
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Mathematical morphology Operators
Operators on Lena
Figure 8: Original Lena
Figure 9: Dilation and Erosion
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Mathematical morphology Operators
Operators on Lena
Figure 10: Closing and Opening
Figure 11: Black and White Tophat
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Restoration of vertical scratches
Restoration of vertical scratches
Characteristics :
• vertical lines
• long
• narrow
• brighter than neighbour
• do not move with time
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Restoration of vertical scratches
Detection - Modelisation
• dx < Dx
• dy < Dy
• the artifact is a local maxima
• dt < Dt
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Restoration of vertical scratches
Detection - Algorithm 1
1. Horizontal white tophat with size Dx
2. Vertical opening with size Dy
3. Time opening with size Dt
4. Space-time reconstruction
5. Threshold
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Restoration of vertical scratches
Detection - Algorithm 1 : result
Figure 12: Image source
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Restoration of vertical scratches
Detection - Algorithm 1 : result
Figure 13: White tophat and 2D Opening
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Restoration of vertical scratches
Detection - Algorithm 1 : result
Figure 14: 3D opening and Threshold
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Restoration of vertical scratches
Detection - Algorithm 1 : drawbacks
• images are often noisy
. vertical scratches are discontinuted
• we can have near-vertical lines in the image
. false detections
• motion is often small
. Time opening is not so useful
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Restoration of vertical scratches
Detection - Algorithm 2
1. Horizontal white tophat with size Dx (a)
2. Column accumulator
3. Horizontal white tophat with size Dx on accumulator
4. Threshold on accumulator with g1 (b)
5. Threshold on (a) with g2, g2 < g1 (c)
6. Reconstruction of vertical lines using (b) as “column marker” on (c)
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Restoration of vertical scratches
Detection - Algorithm 2 : result
Figure 15: Vertical scratches - Algorithm 2
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Restoration of vertical scratches
Detection - Algorithm 2 : result
Figure 16: Vertical scratches - Detection result
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Restoration of vertical scratches
Restoration
• We don’t have time information
. we have to work only in 2D
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Restoration of vertical scratches
Restoration - “algorithm”
1. Neighbour pixels can be deteriorate by the artifact. Dilation of mask using C8 connexity
2. Horizontal opening on original image with size Dx
3. “cut and paste” using mask
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Restoration of vertical scratches
Restoration - sample
Figure 17: Restoration sample
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Restoration of local random defects
Restoration of local random defects
Figure 18: Image sample
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Restoration of local random defects
Detection - first approach
1. Detect on each frame potential local random defects
2. Apply time criteria to reject false detections
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Restoration of local random defects
Detection - Modelisation
Characteristics:
• Maximal area Smax
• Minimal area Smin
• Minimal constrast g
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Restoration of local random defects
Detection - Algorithm
Input F:
1. Maxima killing with area Smax ⇒ F1
2. F2 = F − F1
. F2 has only maxima with area < Smax
3. Threshold F2 with g ⇒ F3
4. Reconstruction using F2 as mask and F3 as mark ⇒ F4
5. Binarize of F4 using glow ⇒ F5
6. Killing of area < Smin
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Restoration of local random defects
Detection - Results
Figure 19: Original Image
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Restoration of local random defects
Detection - Results
Figure 20: Maxima and Threshold
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Restoration of local random defects
Detection - Results
Figure 21: Reconstruction and final frame detection
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Restoration of local random defects
Detection - Results
Figure 22: Time analysis and final mask
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Restoration of local random defects
Detection - Algorithm 1 drawback
• Time consuming
• False detections
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Restoration of local random defects
Detection - Second approach
1. Space-Time analysis
2. Post-processing
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Restoration of local random defects
Detection - Algorithm 2
1. Space-Time opening ⇒ I1
2. I2 = I − I1
3. Threshold I2 with glow ⇒ B1
4. Threshold I2 with ghigh ⇒ B2
5. Reconstruction using B1 as mask and B2 as mark
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Restoration of local random defects
Detection - Results
Figure 23: Space-time opening and difference
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Restoration of local random defects
Detection - Results
Figure 24: Threshold glow and ghigh
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Restoration of local random defects
Detection - Results
Figure 25: Final detection mask
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Restoration of local random defects
Restoration
• Tiny defects: gaussian for instance
• Medium defects: we can use Fourier domain
• Big defects: we have to use time informations
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Restoration of local random defects
Restoration sample
Figure 26: Restoration sample
. Maybe we can adapt g1 and g2 for each frame
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Conclusion
Conclusion
• Implementation of other time connexity criteria
• Implementation of Fourier domain restoration method
• Implementation of motion estimations
• Try to adapt glow and ghigh for each frame in local algorithm 2
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