Ecole Centrale, Grande Voie des Vignes, 92 295 Chatenay-Malabry, France.
IEEE Trans Image Process. 2010 May;19(5):1181-90. doi: 10.1109/TIP.2009.2037468. Epub 2009 Dec 1.
In this paper, we introduce a reconstruction framework that explicitly accounts for image geometry when defining the spatial interaction between pixels in the filtering process. To this end, image structure is captured using local co-occurrence statistics and is incorporated to the enhancement algorithm in a sequential fashion using the particle filtering technique. In this context, the reconstruction process is modeled using a dynamical system with multiple states and its evolution is guided by the prior density describing the image structure. Towards optimal exploration of the image geometry, an evaluation process of the state of the system is performed at each iteration. The resulting framework explores optimally spatial dependencies between image content towards variable bandwidth image reconstruction. Promising results using additive noise models demonstrate the potentials of such an explicit modeling of the geometry.
在本文中，我们引入了一种重建框架，在滤波过程中定义像素之间的空间相互作用时明确考虑到图像几何形状。为此，使用局部共现统计来捕获图像结构，并使用粒子滤波技术以顺序方式将其合并到增强算法中。在这种情况下，使用具有多个状态的动力系统对重建过程进行建模，并且其演化由描述图像结构的先验密度来指导。为了对图像几何形状进行最佳探索，在每次迭代时都对系统状态进行评估过程。由此得到的框架针对可变带宽图像重建，对图像内容之间的空间相关性进行了最佳探索。使用加性噪声模型的令人鼓舞的结果证明了这种显式几何建模的潜力。
