  Joint image filters leverage the guidance image as a prior and transfer the
structural details from the guidance image to the target image for suppressing
noise or enhancing spatial resolution. Existing methods either rely on various
explicit filter constructions or hand-designed objective functions, thereby
making it difficult to understand, improve, and accelerate these filters in a
coherent framework. In this paper, we propose a learning-based approach for
constructing joint filters based on Convolutional Neural Networks. In contrast
to existing methods that consider only the guidance image, the proposed
algorithm can selectively transfer salient structures that are consistent with
both guidance and target images. We show that the model trained on a certain
type of data, e.g., RGB and depth images, generalizes well to other modalities,
e.g., flash/non-Flash and RGB/NIR images. We validate the effectiveness of the
proposed joint filter through extensive experimental evaluations with
state-of-the-art methods.
