Edge/structure-preserving operations for images aim to smooth images without blurring the edges/structures. Many exemplary edge-preserving filtering methods have recently been proposed to reduce the computational complexity and/or separate structures of different scales. They normally adopt a user-selected scale measurement to control the detail smoothing. However, natural photos contain objects of different sizes, which cannot be described by a single scale measurement. On the other hand, contour analysis is closely related to edge-preserving filtering, and significant progress has recently been achieved. Nevertheless, the majority of state-of-the-art filtering techniques have ignored the successes in this area. Inspired by the fact that learning-based edge detectors significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the differential operations in edge-preserving filters with the effectiveness of the recent edge detectors for scale-aware filtering. Unlike previous filtering methods, the proposed filters can efficiently extract subjectively meaningful structures from natural scenes containing multiple-scale objects.

中文翻译：

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联合轮廓过滤

图像的边缘/结构保留操作旨在平滑图像而不模糊边缘/结构。最近已经提出了许多示例性边缘保留滤波方法来降低计算复杂度和/或不同尺度的分离结构。它们通常采用用户选择的尺度测量来控制细节平滑。然而，自然照片包含不同大小的物体，不能用单一的尺度测量来描述。另一方面，轮廓分析与保边滤波密切相关，最近取得了重大进展。尽管如此，大多数最先进的过滤技术都忽略了该领域的成功。受基于学习的边缘检测器明显优于传统手动设计检测器这一事实的启发，本文提出了一种基于学习的边缘保留过滤技术。它将边缘保留滤波器中的微分运算与最近的边缘检测器用于尺度感知滤波的有效性相结合。与以前的过滤方法不同，所提出的过滤器可以从包含多尺度对象的自然场景中有效地提取主观上有意义的结构。