Motion-Compensated Noise Reduction of B &W Motion Picture Films EE392J Final Project ZHU Xiaoqing March, 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 Motion Estimation Write out Frames Read in Frames MC Filtering 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: 1 st step: “change” detection Criterion: Mean Absolute Difference(MAD) & “Edgy Area” Original frame vs. filtered frame 2 nd 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