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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