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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:
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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: • 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
Characteristic of Blotchy Noise Typical Blotches • They are: • Arbitrary shape & size • Obvious contrast against background • Non-persisting in position • They might NOT: • Be purely black/white • Have clear border
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
‘sandwiched’ MC Filtering Write out Frames Read in Frames A Motion Estimation Temporal Median Filter B Pixel-wise Frame-wise Window=5 Section-wise Blotch Detection Motion Detection ProposedScheme
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)
