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A design framework for hybrid approaches of image noise estimation and its application to noise reduction

机译:图像噪声估计混合方法的设计框架及其在降噪中的应用

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摘要

Noise estimation is an important process in digital imaging systems. Many noise reduction algorithms require their parameters to be adjusted based on the noise level. Filter-based approaches of image noise estimation usually were more efficient but had difficulty on separating noise from images. Block-based approaches could provide more accurate results but usually required higher computation complexity. In this work, a design framework for combining the strengths of filter-based and block-based approaches is presented. Different homogeneity analyzers for identifying the homogeneous blocks are discussed and their performances are compared. Then, two well-known filters, the bilateral and the non-local mean, are reviewed and their parameter settings are investigated. A new bilateral filter with edge enhancement is proposed. A modified non-local mean filter with much less complexity is also present. Compared to the original non-local mean filter, the complexity is dramatically reduced by 75% and yet the image quality is maintained.
机译:噪声估计是数字成像系统中的重要过程。许多降噪算法要求根据噪声水平调整其参数。基于滤波器的图像噪声估计方法通常更有效,但很难将噪声与图像分离。基于块的方法可以提供更准确的结果,但通常需要更高的计算复杂度。在这项工作中,提出了一个组合基于滤波器和基于块的方法的优点的设计框架。讨论了用于识别均质块的不同均质分析器,并对它们的性能进行了比较。然后,回顾了两个众所周知的过滤器,即双边和非局部均值,并研究了它们的参数设置。提出了一种新的具有边缘增强的双边滤波器。还提出了一种复杂性大大降低的改进的非局部均值滤波器。与原始的非局部均值滤波器相比,复杂度显着降低了75%,但仍保持了图像质量。

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