+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences

Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences

IEEE Transactions on Image Processing 4(9): 1236-1251

We develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and the intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal noise filters treat the estimation of the DVF as a preprocessing step. Generally, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, we establish a link between the two estimators. It is through this link that the DVF estimate and its corresponding accuracy information are shared with the other intensity estimator, and vice versa. To model the DVF and the intensity field, we use coupled Gauss-Markov (CGM) models. A CGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line field, which governs the transitions between the submodels. The CGM models are well suited for estimating the displacement and intensity fields since the resulting estimates preserve the boundaries between the stationary areas present in both fields. Detailed line fields are proposed for the modeling of these boundaries, which also take into account the correlations that exist between these two fields. A Kalman-type estimator results, followed by a decision criterion for choosing the appropriate set of line fields. Several experiments using noisy and noisy-blurred image sequences demonstrate the superior performance of the proposed algorithm with respect to prediction error and mean-square error.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 055791265

Download citation: RISBibTeXText

PMID: 18292020

DOI: 10.1109/83.413168

Related references

Efficient multiframe Wiener restoration of blurred and noisy image sequences. IEEE Transactions on Image Processing 1(4): 453-476, 1992

Image Restoration with Multiple Hard Constraints on Data-Fidelity to Blurred/Noisy Image Pair. Ieice Transactions on Information and Systems E100.D(9): 1953-1961, 2017

Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 6(12): 1646-1658, 1997

A Recursive Digital Filter Implementation for Noisy and Blurred Images. Real-Time Imaging 4(3): 181-191, 1998

Restoration of space-variant blurred image based on motion-blurred target segmentation. Journal of Systems Engineering and Electronics 21(2): 191-196, 2010

3-d motion estimation, understanding, and prediction from noisy image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(3): 370-389, 1987

Restoration of noisy blurred images by a smoothing spline filter. Applied Optics 16(12): 3147-3153, 1977

Motion estimation and compensation from noisy image sequences: A new filtering scheme. Image and Vision Computing 25(5): 686-694, 2007

Reconstruction of an axisymmetric image from its blurred and noisy projection. Applied Optics 30(14): 1811-1819, 1991

Blurred and noisy image pairs in parallel optics. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 31(11): 2529-2537, 2014

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model. Plos One 10(4): E0120885, 2016

Hierarchical Estimation of Displacement Vectors in Image Sequences. Systems and Computers in Japan 19(10): 1-7, 1988

An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(5): 565-593, 1986

Simultaneous multichannel image restoration and estimation of the regularization parameters. IEEE Transactions on Image Processing 6(5): 774-778, 1997

Iterative maximum likelihood displacement field estimation in quantum-limited image sequences. IEEE Transactions on Image Processing 4(6): 743-751, 1995