Motion-Compensated Noise Reduction of B &W Motion Picture Films EE392J Final Project ZHU Xiaoqing March, 2002.

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