By benefiting from perceptual losses, recent studies have improved
significantly the performance of the super-resolution task, where a
high-resolution image is resolved from its low-resolution counterpart. Although
such objective functions generate near-photorealistic results, their capability
is limited, since they estimate the reconstruction error for an entire image in
the same way, without considering any semantic information. In this paper, we
propose a novel method to benefit from perceptual loss in a more objective way.
We optimize a deep network-based decoder with a targeted objective function
that penalizes images at different semantic levels using the corresponding
terms. In particular, the proposed method leverages our proposed OBB (Object,
Background and Boundary) labels, generated from segmentation labels, to
estimate a suitable perceptual loss for boundaries, while considering texture
similarity for backgrounds. We show that our proposed approach results in more
realistic textures and sharper edges, and outperforms other state-of-the-art
algorithms in terms of both qualitative results on standard benchmarks and
results of extensive user studies.

利用像素级的知觉损失优化高分辨率图像的生成，本研究使用基于深度卷积神经网络的解码器进行优化，通过对不同语义级别的标签惩罚来提高性能并考虑到背景和边界的纹理相似性，得到更逼真的纹理和更锐利的边缘，实验表明此方法的效果优于现有的其他算法。