Film and video restoration using rank-order models

• Main Author: Armstrong, S.
• Published: University of Cambridge 1999
• Subjects: 502.85
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596155

This thesis develops a formalism for an image model that is based on rank-order operators. More commonly used as filters, the rank-order operators are here employed as predictors. A Laplacian excitation sequence is chosen to complete the model. Images are generated with the model and compared with those formed with an AR model. A multidimensional rank-order model is formed from vector medians for use with multidimensional image data. The first application using the rank-order model is an impulsive noise detector. This exploits the notion of 'multimodality' in the histogram of a difference image of the degraded image and a rank-order filtered version. It uses the EM algorithm and a mixture model to automatically determine thresholds for detecting the impulsive noise. This method compares well with other detection methods, which require manual setting of thresholds, and to stack filtering, which requires an undegraded training sequence. The impulsive noise detector is developed further to detect and remove degradation caused by scratches on 2-inch video tape. Additional techniques are developed to correct other defects such as line jitter and line fading. The second half of the thesis is concerned with reconstructing missing regions in images and image sequences. First of all an interpolation method is developed based on rank-order predictors. This proves to be very computationally intensive, but the rank-order structure is shown to reconstruct image features well, doing remarkably well on edges. A method using the Gibbs sampler for reconstructing missing data in images is developed and results show that convergence is very rapid. Motion estimation and automatic detection of missing data is added to produce a method for automatically detecting and reconstructing missing data in image sequences. Reconstructions are of a very high quality and the method compares very well with similar AR based sampling methods. Improved reconstruction of edges is again observed.