Efficient Computation of Convolution of Huge Images

In image processing, convolution is a frequently used operation. It is an important tool for performing basic image enhancement as well as sophisticated analysis. Naturally, due to its necessity and still continually increasing size of processed image data there is a great demand for its efficient implementation. The fact is that the slowest algorithms (that cannot be practically used) implementing the convolution are capable of handling the data of arbitrary dimension and size. On the other hand, the fastest algorithms have huge memory requirements and hence impose image size limits. Regarding the convolution of huge images, which might be the subtask of some more sophisticated algorithm, fast and correct solution is essential. In this paper, we propose a fast algorithm implementing exact computation of the shift invariant convolution over huge multi-dimensional image data.

[1]  Don H. Johnson,et al.  Gauss and the history of the fast Fourier transform , 1984, IEEE ASSP Magazine.

[2]  Heinz-Otto Peitgen,et al.  Ground Truth in MS Lesion Volumetry - A Phantom Study , 2003, MICCAI.

[3]  Steven W. Smith CHAPTER 28 – Digital Signal Processors , 2002 .

[4]  Jiri Jan Digital Signal Filtering, Analysis and Restoration (Telecommunications Series) , 2000 .

[5]  Terry M. Peters,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 , 2003, Lecture Notes in Computer Science.

[6]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[7]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.

[8]  Jiří Jan,et al.  Digital signal filtering, analysis and restoration , 2000 .

[9]  P. J. Verveer,et al.  Computational and optical methods for improving resolution and signal quality in fluorescence microscopy , 1998 .

[10]  Michal Kozubek,et al.  Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[11]  Jørgen Arendt Jensen,et al.  Computer Phantoms for Simulating Ultrasound B-Mode and CFM Images , 1997 .

[12]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[13]  William K. Pratt,et al.  Digital image processing (2nd ed.) , 1991 .