一种基于分片插值模型和广义Gamma分布的引导边缘平滑锐化滤波器
A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized Gamma distribution
摘要：
平滑和锐化是两种基本的图像处理操作。后者通常通过反锐化掩模算法与前者相关。在本文中，我们开发了一种新型的滤波器，通过调整参数进行平滑或锐化。新滤波器的开发基于（1）统一平滑和锐化操作的新的基于拉普拉斯的滤波器公式，（2）与提供边缘感知能力的引导滤波器中使用的模型类似的补丁插值模型，以及（3）用作参数估计的先验的广义伽马分布。我们已经进行了详细的研究，两个版本的建议过滤器（自我指导和外部指导）的属性。我们还进行了实验，以证明所提出的过滤器的应用。在自我指导的情况下，我们已经开发出自适应平滑和锐化算法的基础上，纹理，深度和模糊信息提取的图像。应用包括增强人脸图像，产生浅景深效果，基于焦点的图像增强和接缝雕刻。在外部指导的情况下，我们已经开发了新的算法，闪光灯和无闪光灯的图像相结合，并使用全色图像增强多光谱图像。
英文摘要：
Smoothing and sharpening are two fundamental image processing operations. The latter is usually related to the former through the unsharp masking algorithm. In this paper, we develop a new type of filter which performs smoothing or sharpening via a tuning parameter. The development of the new filter is based on (1) a new Laplacian-based filter formulation which unifies the smoothing and sharpening operations, (2) a patch interpolation model similar to that used in the guided filter which provides edge-awareness capability, and (3) the generalized Gamma distribution which is used as the prior for parameter estimation. We have conducted detailed studies on the properties of two versions of the proposed filter (self-guidance and external guidance). We have also conducted experiments to demonstrate applications of the proposed filter. In the self-guidance case, we have developed adaptive smoothing and sharpening algorithms based on texture, depth and blurriness information extracted from an image. Applications include enhancing human face images, producing shallow depth of field effects, focus-based image enhancement, and seam carving. In the external guidance case, we have developed new algorithms for combining flash and no-flash images and for enhancing multi-spectral images using a panchromatic image.
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