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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Selecting good regions to deblur via relative total variation
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Selecting good regions to deblur via relative total variation

机译：通过相对总变化选择好的区域进行去模糊

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摘要

Image deblurring is to estimate the blur kernel and to restore the latent image. It is usually divided into two stage, including kernel estimation and image restoration. In kernel estimation, selecting a good region that contains structure information is helpful to the accuracy of estimated kernel. Good region to deblur is usually expert-chosen or in a trial-and-error way. In this paper, we apply a metric named relative total variation (RTV) to discriminate the structure regions from smooth and texture. Given a blurry image, we first calculate the RTV of each pixel to determine whether it is the pixel in structure region, after which, we sample the image in an overlapping way. At last, the sampled region that contains the most structure pixels is the best region to deblur. Both qualitative and quantitative experiments show that our proposed method can help to estimate the kernel accurately.
机译：图像去模糊是为了估计模糊核并恢复潜像。它通常分为两个阶段，包括内核估计和图像恢复。在核估计中，选择一个包含结构信息的良好区域有助于估计核的准确性。通常，由专家选择或以反复试验的方式来选择要模糊的良好区域。在本文中，我们应用名为相对总变化（RTV）的量度来区分结构区域与平滑度和纹理。给定模糊的图像，我们首先计算每个像素的RTV以确定它是否是结构区域中的像素，然后，我们以重叠的方式对图像进行采样。最后，包含最多结构像素的采样区域是去模糊的最佳区域。定性和定量实验均表明，我们提出的方法可以帮助准确估计内核。

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