We aim to super-resolve digital paintings, synthesizing realistic details
from high-resolution reference painting materials for very large scaling
factors (e.g., 8X, 16X). However, previous single image super-resolution (SISR)
methods would either lose textural details or introduce unpleasing artifacts.
On the other hand, reference-based SR (Ref-SR) methods can transfer textures to
some extent, but is still impractical to handle very large factors and keep
fidelity with original input. To solve these problems, we propose an efficient
high-resolution hallucination network for very large scaling factors with an
efficient network structure and feature transferring. To transfer more detailed
textures, we design a wavelet texture loss, which helps to enhance more
high-frequency components. At the same time, to reduce the smoothing effect
brought by the image reconstruction loss, we further relax the reconstruction
constraint with a degradation loss which ensures the consistency between
downscaled super-resolution results and low-resolution inputs. We also
collected a high-resolution (e.g., 4K resolution) painting dataset PaintHD by
considering both physical size and image resolution. We demonstrate the
effectiveness of our method with extensive experiments on PaintHD by comparing
with SISR and Ref-SR state-of-the-art methods.

本文提出了一种高效的大比例尺率高清晰度画作超分辨率算法，其中使用了小波纹理损失和退化损失来转移更多的纹理信息。同时，还使用高清晰度画作数据集 PaintHD 和其他方法的比较来证明本算法的有效性。