Texture Preserving Variational Denoising Using an Adaptive Fidelity Term
Texture Preserving Variational Denoising Using an Adaptive Fidelity Term
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发表时间：
2003
影响因子：
1
通讯作者：
Guy Gilboa;Y. Zeevi;N. Sochen
中科院分区：
文献类型：
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作者：
Guy Gilboa;Y. Zeevi;N. Sochen
基于梯度相关能量泛函的去噪算法，如Perona-Malik和全变分去噪，将图像修改为分段常数函数。虽然边缘清晰度和位置得到了很好的保留，但编码在图像特征（如纹理或某些细节）中的重要信息往往在去噪过程中受到损害。我们提出了一种机制，更好地保留精细尺度的功能，在这样的去噪过程。这是通过添加一个空间变化的保真度项来实现的，该保真度项根据图像区域的内容来局部控制图像区域的去噪程度。信号的振荡部分的局部方差测量用于计算自适应保真度项。我们的研究结果表明，改善的信号音调比标量保真度长期的过程，他们更吸引人的视觉。这种类型的处理相对简单，可用于基于PDE的图像处理和计算机视觉中的各种任务，并且从数学角度来看是稳定和有意义的。
Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik and total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. We propose a mechanism that better preserves fine scale features in such denoising processes. This is accomplished by adding a spatially varying fidelity term that locally controls the extent of denoising over image regions according to their content. Local variance measures of the oscillatory part of the signal are used to compute the adaptive fidelity term. Our results show improvement in the signal-tonoise ratio over scalar fidelity term processes, and they are more appealing visually. This type of processing is relatively simple, can be used for a variety of tasks in PDE-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.