Joint image filters are used to transfer structural details from a guidance
picture used as a prior to a target image, in tasks such as enhancing spatial
resolution and suppressing noise. Previous methods based on convolutional
neural networks (CNNs) combine nonlinear activations of spatially-invariant
kernels to estimate structural details and regress the filtering result. In
this paper, we instead learn explicitly sparse and spatially-variant kernels.
We propose a CNN architecture and its efficient implementation, called the
deformable kernel network (DKN), that outputs sets of neighbors and the
corresponding weights adaptively for each pixel. The filtering result is then
computed as a weighted average. We also propose a fast version of DKN that runs
about seventeen times faster for an image of size 640 x 480. We demonstrate the
effectiveness and flexibility of our models on the tasks of depth map
upsampling, saliency map upsampling, cross-modality image restoration, texture
removal, and semantic segmentation. In particular, we show that the weighted
averaging process with sparsely sampled 3 x 3 kernels outperforms the state of
the art by a significant margin in all cases.

该论文提出了 DKN 卷积神经网络架构及其高效实现，使用稀疏且空间可变的滤波核进行图像增强和降噪，并在深度图上采样，显著优于现有技术。

可变形核网络用于联合图像滤波

Deformable Kernel Networks for Joint Image Filtering

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.

本研究提出了一种学习性方法，基于卷积神经网络构建联合滤波器，该算法可以选择性地传输与指导和目标图像一致的重要结构，验证表明该联合滤波器在抑制噪声或增强空间分辨率方面具有有效性，并且具有与现有最先进方法相当的性能。