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An Adaptive Total Variation Model with Local Constraints for Denoising Partially Textured Images

机译:具有局部约束的自适应总变化模型对部分纹理图像去噪

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Denoising algorithms such as Total Variation model modify smooth areas in images into piecewise constant patches and small scale details and textures present in the original image are not preserved satisfactorily by these processes. In this paper, we present an algorithm based on an adaptive Total Variation norm of the gradient of the image, with a family of local constraints for efficient denoising of natural images. In fact, natural images consist of smooth and textured regions. Staircase effect is reduced in smooth areas by using a modified Total Variation functional. The set of local constraints, one for each pixel in the image are able to preserve most of the fine details and textures in the images. Visual and quantitative results of proposed method are presented and are compared with results of existing methods.
机译:诸如总变化模型之类的去噪算法将图像中的平滑区域修改为分段恒定的小块,并且原始图像中存在的小比例细节和纹理无法通过这些过程令人满意地保留。在本文中,我们提出了一种基于图像梯度的自适应总变分范数的算法,该算法具有一系列有效地对自然图像进行降噪的局部约束。实际上,自然图像由平滑和纹理化的区域组成。通过使用改良的“总变化”功能,可以减少光滑区域的楼梯效果。一组局部约束(对于图像中的每个像素一个)可以保留图像中的大多数精细细节和纹理。给出了所提出方法的视觉和定量结果,并将其与现有方法的结果进行了比较。

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