Autocorrelation-Driven Diffusion Filtering

The dataset consists of Matlab code and present a novel scheme for anisotropic diffusion driven by the image autocorrelation function. We show the equivalence of this scheme to a special case of iterated adaptive filtering. By determining the diffusion tensor field from an autocorrelation estimate, we obtain an evolution equation that is computed from a scalar product of diffusion tensor and the image Hessian. We propose further a set of filters to approximate the Hessian on a minimized spatial support. On standard benchmarks, the resulting method performs favorable in many cases, in particular at low noise levels. In a GPU implementation, video real-time performance is easily achieved. The dataset was originally published in DiVA and moved to SND in 2024.本数据集内含Matlab代码，提出了一种由图像自相关函数驱动的各向异性扩散新方案。我们证明了该方案等价于迭代自适应滤波的一种特殊情形。通过自相关估计得到扩散张量场，我们推导出了由扩散张量与图像海森（Hessian）矩阵的标量积计算得到的演化方程。我们进一步提出了一组滤波器，可在最小化的空间支撑域上近似计算海森矩阵。在标准基准测试集上，所提方法在多数场景下表现优异，尤其在低噪声环境中效果突出。若采用GPU实现，该方法可轻松实现视频实时处理。 本数据集最初发布于DiVA平台，并于2024年迁移至SND平台。