A low-complexity 3D spatio-temporal FIR filter for enhancing linear trajectory signals

A low-complexity 3D FIR filter is proposed for selectively enhancing severely corrupted linear trajectory spatio-temporal signals. The proposed 3D FIR filter is separable and consists of a 3D spatio-temporal wide-angle FIR cone filter between two 2D spatial variable-shift filters. Low complexity of the 3D spatio-temporal wide-angle FIR cone filter is achieved by exploiting the spatial-symmetry of the passband and by employing maximal decimation in the temporal dimension. Compared to existing techniques, the proposed 3D FIR filter provides significant reduction of the computational complexity for similar SINR improvement.

[1]  Leonard T. Bruton,et al.  Low-Complexity Maximally-Decimated Multirate 3-D Spatio-Temporal FIR Cone and Frustum Filters , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  A.K. Krishnamurthy,et al.  Multidimensional digital signal processing , 1985, Proceedings of the IEEE.

[3]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[4]  Tanja Karp,et al.  Modified DFT filter banks with perfect reconstruction , 1999 .

[5]  Henrique S. Malvar Extended lapped transforms: properties, applications, and fast algorithms , 1992, IEEE Trans. Signal Process..

[6]  Leonard T. Bruton,et al.  Three-dimensional image processing using the concept of network resonance , 1985 .

[7]  Leonard T. Bruton,et al.  Using 1-D Variable Fractional-Delay Filters to Reduce the Computational Complexity of 3-D Broadband Multibeam Beamformers , 2014, IEEE Transactions on Circuits and Systems II: Express Briefs.

[8]  Anton Kummert,et al.  A multidimensional wave digital filter bank for video-based motion analysis , 2014, Multidimens. Syst. Signal Process..

[9]  Anton Kummert,et al.  Motion-Based Object Detection for Automotive Applications using Multidimensional Wave Digital Filters , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[10]  Nozomu Hamada,et al.  Design of optimal filter for detecting linear trajectory signals utilizing object shape and velocity vector , 2000 .

[11]  Liang-Gee Chen,et al.  Relative Depth Layer Extraction for Monoscopic Video by Use of Multidimensional Filter , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[12]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[13]  L.T. Bruton,et al.  On the classification of moving objects in image sequences using 3D adaptive recursive tracking filters and neural networks , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[14]  Leonard T. Bruton,et al.  The enhancement and tracking of moving objects in digital images using adaptive three-dimensional recursive filters , 1986 .

[15]  Soo-Chang Pei,et al.  Tracking moving objects in image sequences using 1-D trajectory filter , 2006, IEEE Signal Processing Letters.

[16]  Leonard T. Bruton,et al.  Non-uniform bandwidth frequency-planar (NUB-FP) filter banks , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[17]  Leonard T. Bruton,et al.  A novel low-complexity spatio-temporal ultra wide-angle polyphase cone filter bank applied to sub-pixel motion discrimination , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[18]  Leonard T. Bruton,et al.  3-D IIR filtering using decimated DFT-polyphase filter bank structures , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[19]  Yao Wang,et al.  Video Processing and Communications , 2001 .

[20]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .