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A method of constructing motion-blurred image based on weighted accumulation of subimages

机译:一种基于子图像加权累积的运动模糊图像构建方法

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A new approach of degrading image is presented. This method has four steps: obtain the number of the subimages first, and then these subimages are constructed, the third step is calculating the weight of each subimage according to its velocity, at last motion-blurred image is constructed by weighted accumulating the subimages. We compared the effects between the method of this article and existing algorithms by using accelerated motion-blurred images and retrace motion-blurred images separately. The maximum absolute differences between the accelerated motion-blurred images degraded by the method of this article and the existing algorithms which are discrete convolution (DC) and discrete Fourier transform (DFT) are 2.5580× 10~(-13) and 3.4106× 10~(-13) separately. And the maximum absolute differences are 2.5580× 10~(-13) and 3.4106× 10~(-13) separately for retrace motion-blurred images. It can be proved that the performances of different methods are the same. The time consumed by degrading images of different forms of motion is less than half a second, close to the time consumed by DFT, but hundreds of seconds shorter than DC. What's more, the process of the method of this article strongly resembles the real imaging process, so it can be comprehended more easily than the existing algorithms.
机译:提出了一种降低图像质量的新方法。该方法包括四个步骤:首先获得子图像的数量,然后构造这些子图像,第三步是根据每个子图像的速度计算权重,最后通过加权累积子图像来构造运动模糊图像。通过分别使用加速运动模糊图像和回溯运动模糊图像,我们比较了本文方法和现有算法之间的效果。通过本文的方法进行降级处理后的加速运动模糊图像与现有算法(离散卷积(DC)和离散傅里叶变换(DFT))之间的最大绝对差为2.5580×10〜(-13)和3.4106×10〜 (-13)分开。对于运动模糊图像,最大绝对差分别为2.5580×10〜(-13)和3.4106×10〜(-13)。可以证明,不同方法的性能是相同的。降解不同形式的运动图像所消耗的时间少于半秒,接近DFT所消耗的时间,但比DC短数百秒。而且,本文方法的过程与真实成像过程非常相似,因此比现有算法更容易理解。

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