Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Processing and Communications
Video Processing and Communications
Moment Forms Invariant to Rotation and Blur in Arbitrary Number of Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Full-Frame Video Stabilization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multichannel blind deconvolution of spatially misaligned images
IEEE Transactions on Image Processing
Weighted DFT Based Blur Invariants for Pattern Recognition
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Object recognition using frequency domain blur invariant features
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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This paper deals with concealment of motion blur in image sequences. The approach is different from traditional methods, which attempt to deblur the image. Our approach utilizes the information in consecutive frames, replacing blurred areas of the images with corresponding sharp areas from the previous frames. Blurred but otherwise unchanged areas of the images are recognized using blur invariant features. A statistical approach for calculating the weights for the blur invariant features in frequency and spatial domains is also proposed, and compared to the unweighted invariants in an ideal setting. Finally, the performance of the method is tested using a real blurred image sequence. The results support the use of our approach with the weighting scheme.