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Interpolation of missing data in image sequences.
A C Kokaram1, R D Morris, W J Fitzgerald
1Dept. of Eng., Cambridge Univ.
Summary
This study introduces advanced model-based interpolation methods for reconstructing missing image regions in archived films, outperforming traditional median filters for clearer image restoration.
Area of Science:
- Digital image processing
- Computer vision
- Signal processing
Background:
- Archived film material often suffers from missing regions due to abrasion or obscuration.
- These imperfections manifest as bright and dark flashes, known as "dirt and sparkle," impacting image quality.
- Existing methods like median filters struggle with the arbitrary size and location of these missing areas.
Purpose of the Study:
- To develop and evaluate novel model-based interpolation schemes for reconstructing missing regions in image sequences.
- To address the specific challenge of "dirt and sparkle" in motion picture archives.
- To compare the proposed methods against existing techniques, such as multilevel median filters.
Main Methods:
- Utilizing 3-D autoregressive models and 3-D Markov random fields for reconstruction.
- Estimating motion directions with a multiresolution block matching scheme.
- Implementing and comparing model-based interpolation with multilevel median filters.
Main Results:
- The proposed model-based interpolation schemes demonstrate superior reconstruction fidelity compared to multilevel median filters.
- The methods effectively handle missing regions of arbitrary size and known location.
- Successful suppression of impulsive noise characteristic of "dirt and sparkle" was achieved.
Conclusions:
- Model-based interpolation using 3-D autoregressive models and Markov random fields offers a significant improvement for restoring degraded image sequences.
- These techniques provide a robust solution for preserving and enhancing archived film content.
- The developed interpolators achieve higher fidelity in image reconstruction, particularly for "dirt and sparkle" artifacts.
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