motion-compensated noise reduction of b &w motion picture films
DESCRIPTION
Motion-Compensated Noise Reduction of B &W Motion Picture Films. EE392J Final Project ZHU Xiaoqing March, 2002. My Work. Background/Motivation. Digitization of conventional video data Achieving motion picture films Major artifacts of B&W motion picture films: - PowerPoint PPT PresentationTRANSCRIPT
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Motion-Compensated Noise Reduction of B &W Motion Picture Films
EE392J Final ProjectZHU XiaoqingMarch, 2002
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Background/Motivation• Digitization of conventional video data
• Achieving motion picture films• Major artifacts of B&W motion picture films:
• Blotches: “dirty” spots and patches• Scratch lines• Intensity instability(illumination fluctuation) …
• Previous work• General denoising: joint filtering• Line Scratch: model-based detection & removal • Blotchy noise: seldom addressed specifically
My Work
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Characteristic of Blotchy Noise• They are:
• Arbitrary shape & size• Obvious contrast against
background• Non-persisting in position
• They might NOT:• Be purely black/white• Have clear border
Typical Blotches
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Problems & Challenges• Huge amount of data
• Restrict computational complexity• Automatic processing preferred
• Motion estimation tricked by :• Presence of noise• Illumination Change• Blurry scene for fast motion• …
• Automatic detection not easy• Blotchy noise not readily modeled• Decision rely on motion compensated results
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Proposed Scheme
Blotch Detection
Motion Detection
MotionEstimation
Write out FramesRead in
Frames
MCFiltering
Temporal Median Filter
Section-wise
Pixel-wise
Frame-wise
Window=5
‘sandwiched’
A
B
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Pre-processing• Five-tap temporal median filter• Effectiveness:
• Generally denoising the sequence• Already removed blotchy noises
• Introduced artifacts • Blurring of spatial details at regions w/ motion• missing fast moving lines
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Joint Motion/Noise Detection• Section-wise scanning of each frame
• 8*8 sections, non-overlapped• “sandwiched” decision-making
• Two stage detection:• 1st step: “change” detection
• Criterion: Mean Absolute Difference(MAD) & “Edgy Area”• Original frame vs. filtered frame
• 2nd step: motion or noise• Criterion: ratio of MAD (should be consistent)• Reject changes due to blotchy noise
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Motion Trajectory Estimation• Only computed for detected sections • Dense motion vector field estimation
• Block-matching: • Neighboring block for each pixel: 9*9• Translational model • assuming smoothness of MVF
• Full search• search range (-16, +16)
• weighted MAE criterion• Error weighted by reciprocal of frame difference (A-B)• rejecting noisy data
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Post-processing• Goal: remove artifact with MC-filtering• Available versions of the frame
• Original• Temporally median-filtered• Motion compensated (bi-directional)
• Modification strategy:• Linear combination• Median filter (spatial/temporal/joint)• Hybrid method (with edge information)
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Result Demo
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Result Demo
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Result Demo
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Result Demo