motion-compensated noise reduction of b &w motion picture films
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Motion-Compensated Noise Reduction of B &W Motion Picture Films
EE392J Final ProjectZHU XiaoqingMarch, 2002
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
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
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
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
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
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
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
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)
Result Demo
Result Demo
Result Demo
Result Demo
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