Joint Contour Filtering

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.