Dense pixel-wise image prediction has been advanced by harnessing the
capabilities of Fully Convolutional Networks (FCNs). One central issue of FCNs
is the limited capacity to handle joint upsampling. To address the problem, we
present a novel building block for FCNs, namely guided filtering layer, which
is designed for efficiently generating a high-resolution output given the
corresponding low-resolution one and a high-resolution guidance map. Such a
layer contains learnable parameters, which can be integrated with FCNs and
jointly optimized through end-to-end training. To further take advantage of
end-to-end training, we plug in a trainable transformation function for
generating the task-specific guidance map. Based on the proposed layer, we
present a general framework for pixel-wise image prediction, named deep guided
filtering network (DGF). The proposed network is evaluated on five image
processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that DGF
runs 10-100 times faster and achieves the state-of-the-art performance. We also
show that DGF helps to improve the performance of multiple computer vision
tasks.

本文介绍了一种基于改进 FCN 的新型构建块 —— 引导滤波层，以及结合该层的像素级别图像预测框架 DGF，并在该框架上取得了比现有方法具有更快速度和更高性能的结果。