A MRF model based scheme for accurate detection and adaptive interpolation of missing data in nightly corrupted image sequences

Abstract

This paper proposes a robust spatial-temporal MRF model based scheme for aggressive detection and accurate interpolation of missing data (blotches) in highly corrupted image sequences. The blotches in noise-corrupted image sequences exhibit a temporal discontinuity characteristic, which is used for the detection of blotches. The MRF model addresses the problem of incorrect detection due to poor motion compensation at moving edges, by incorporating a moving-edge detector into a priori model. In highly corrupted image sequences where an aggressive detector is needed, the detection field can be interpreted as three main classes. This classification allows for effective noise-removal. These regions are blotches that are to be interpolated: (a) with the existing motion vector field, (b) requiring motion vector correction, and other (c) falsely detected regions. This results in a novel scheme that effectively subdues noise without corrupting other areas of heavily distorted image sequences.