We propose a new architecture for difficult image processing operations, such
as natural edge detection or thin object segmentation. The architecture is
based on a simple combination of convolutional neural networks with the nearest
neighbor search.
We focus our attention on the situations when the desired image
transformation is too hard for a neural network to learn explicitly. We show
that in such situations, the use of the nearest neighbor search on top of the
network output allows to improve the results considerably and to account for
the underfitting effect during the neural network training. The approach is
validated on three challenging benchmarks, where the performance of the
proposed architecture matches or exceeds the state-of-the-art.

提出了一种新的图像处理架构，它基于卷积神经网络与最近邻搜索的简单相结合，并在三个具有挑战性的基准测试中验证其性能与超越现有技术水平。该方法侧重于解决那些难以通过神经网络明确学习的图像转换问题，在此情况下，通过对网络输出进行最近邻搜索可以显著提高结果，并考虑神经网络训练期间的欠拟合效应。