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Accelerating Deconvolution in Extended Depth of Field Based on FFT and GPU Computation

Published: 04 June 2021 Publication History

Abstract

Image Fusion Technology is an effective way to extend the depth of field (EDF) and has a wide range of applications. In [1], Michael Unser et al. proposed a model-based deconvolution method for EDF. In this method, the fused clear texture and topography are obtained by solving a nonlinear least-squares minimization model. However, this algorithm has a large quantity of convolution calculation which takes most of the time in the image fusion process. In this paper, the convolutions are accelerated by Fast Fourier Transform (FFT) which can make the complexity change from to, where and are the height and width of fused image in pixels, respectively. Furthermore, CUDA programming is also used to improve calculation speed. Compared with original algorithm, the speed is increased by approximately ten folds, and the output fusion image is identically tantamount except the boundaries.

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ICIGP '21: Proceedings of the 2021 4th International Conference on Image and Graphics Processing
January 2021
231 pages
ISBN:9781450389105
DOI:10.1145/3447587
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 June 2021

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Author Tags

  1. CUDA
  2. Deconvolution
  3. Fast Fourier Transform
  4. Image Fusion

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