Texture Hallucination for Large-Factor Painting Super-Resolution

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12352)


We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8\(\times \), 16\(\times \)). 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 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.


Texture hallucination Large-factor Painting super-resolution Wavelet texture loss Degradation loss 



This work was supported by the Adobe gift fund.

Supplementary material

504444_1_En_13_MOESM1_ESM.pdf (10.7 mb)
Supplementary material 1 (pdf 11004 KB)


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Northeastern UniversityBostonUSA
  2. 2.Adobe ResearchSan JoseUSA

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