Image restoration by 1-D Kalman filtering on oriented image decompositions

This paper introduces a new image restoration method based on a 1-D Kalman filtering. Using the model of tuned channels, the corrupted image is decomposed into a set of perceptual components characterized by different orientations and frequencies. The restoration step is then performed on each component in one dimension following the appropriate orientation with the well-known Kalman algorithm. Since the decomposition provides perfect reconstruction, the restored image is the recomposition of all the restored components. This approach yields relevant results for 2-D blurred images, using 1-D low order models. Unlike traditional 2-D Kalman restoration techniques, its implementation has no excessive computational load.