Fast motion deblurring
Fast motion deblurring
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DOI：
10.1145/1661412.1618491
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发表时间：
2009-12
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影响因子：
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通讯作者：
Sunghyun Cho;Seungyong Lee
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文献类型：
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作者：
Sunghyun Cho;Seungyong Lee
本文提出了一种快速去模糊的方法，可以在几秒钟内从一幅中等大小的图像中产生去模糊的结果。通过引入一种新的预测步骤并使用图像导数而不是像素值，我们在迭代去模糊过程中同时加速了潜在图像估计和核估计。在预测步骤中，我们使用简单的图像处理技术从估计的潜在图像中预测强边缘，这将仅用于核估计。通过这种方法，由于可以在预测中抑制小的反卷积伪影，因此计算上有效的高斯先验变得足以用于反卷积来估计潜在图像。对于核估计，我们用图像导数来表示优化函数，并通过减少共轭梯度法所需的傅里叶变换次数来加速数值过程。我们还表明，与使用像素值相比，该公式导致数值系统的条件数更少，从而获得更快的收敛速度。实验结果表明，我们的方法比以前的工作快一个数量级，而去模糊质量与以前的工作相当。GPU的实现有利于进一步的加速，使我们的方法足够快，可以实际使用。
This paper presents a fast deblurring method that produces a deblurring result from a single image of moderate size in a few seconds. We accelerate both latent image estimation and kernel estimation in an iterative deblurring process by introducing a novel prediction step and working with image derivatives rather than pixel values. In the prediction step, we use simple image processing techniques to predict strong edges from an estimated latent image, which will be solely used for kernel estimation. With this approach, a computationally efficient Gaussian prior becomes sufficient for deconvolution to estimate the latent image, as small deconvolution artifacts can be suppressed in the prediction. For kernel estimation, we formulate the optimization function using image derivatives, and accelerate the numerical process by reducing the number of Fourier transforms needed for a conjugate gradient method. We also show that the formulation results in a smaller condition number of the numerical system than the use of pixel values, which gives faster convergence. Experimental results demonstrate that our method runs an order of magnitude faster than previous work, while the deblurring quality is comparable. GPU implementation facilitates further speed-up, making our method fast enough for practical use.