The bilateral filter is a classical technique for edge-preserving smoothing. It has been widely used as an effective image denoising approach to remove Gaussian noise. The performance of bilateral filtering highly depends on the accuracy of its range distance estimation, which is used for pixel-neighbourhood similarity measurement. However, in the conventional bilateral filtering approach, estimating the range distance directly from noisy observation results in the degradation of denoising performance. To address this issue, the authors propose a novel bilateral filtering scheme with a dual-range kernel, which provides a more robust range of distance estimation at various noise levels compared with existing methods. To further improve the denoising performance, they employ a linear model to retrieve the remaining image details from the method noise and add them back to the denoised image by employing an optimal approach based on Stein's unbiased risk estimate. Experiments on standard test images demonstrate that the proposed method outperforms conventional bilateral filter and its major state-of-the-art variants.

中文翻译：

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具有双范围核的最优加权双边滤波器，用于去除高斯噪声

双边滤波器是保留边缘平滑的经典技术。它已被广泛用作消除高斯噪声的有效图像去噪方法。双边滤波的性能在很大程度上取决于其距离距离估计的准确性，该距离估计用于像素邻域相似性测量。但是，在常规的双边滤波方法中，直接从嘈杂的观测值估计距离距离会导致降噪性能下降。为了解决这个问题，作者提出了一种具有双范围核的新型双边滤波方案，与现有方法相比，该方案在各种噪声水平下都提供了更可靠的距离估计范围。为了进一步提高去噪性能，他们采用线性模型从方法噪声中检索剩余的图像细节，并通过基于斯坦因的无偏风险估计的最佳方法将它们添加回去噪图像中。在标准测试图像上进行的实验表明，所提出的方法优于常规的双边滤波器及其主要的最新技术。