  Guided filter is a fundamental tool in computer vision and computer graphics
which aims to transfer structure information from guidance image to target
image. Most existing methods construct filter kernels from the guidance itself
without considering the mutual dependency between the guidance and the target.
However, since there typically exist significantly different edges in the two
images, simply transferring all structural information of the guidance to the
target would result in various artifacts. To cope with this problem, we propose
an effective framework named deep attentional guided image filtering, the
filtering process of which can fully integrate the complementary information
contained in both images. Specifically, we propose an attentional kernel
learning module to generate dual sets of filter kernels from the guidance and
the target, respectively, and then adaptively combine them by modeling the
pixel-wise dependency between the two images. Meanwhile, we propose a
multi-scale guided image filtering module to progressively generate the
filtering result with the constructed kernels in a coarse-to-fine manner.
Correspondingly, a multi-scale fusion strategy is introduced to reuse the
intermediate results in the coarse-to-fine process. Extensive experiments show
that the proposed framework compares favorably with the state-of-the-art
methods in a wide range of guided image filtering applications, such as guided
super-resolution, cross-modality restoration, texture removal, and semantic
segmentation.
