  Nowadays, online screen sharing and remote cooperation are becoming
ubiquitous. However, the screen content may be downsampled and compressed
during transmission, while it may be displayed on large screens or the users
would zoom in for detail observation at the receiver side. Therefore,
developing a strong and effective screen content image (SCI) super-resolution
(SR) method is demanded. We observe that the weight-sharing upsampler (such as
deconvolution or pixel shuffle) could be harmful to sharp and thin edges in
SCIs, and the fixed scale upsampler makes it inflexible to fit screens with
various sizes. To solve this problem, we propose an implicit transformer
network for continuous SCI SR (termed as ITSRN++). Specifically, we propose a
modulation based transformer as the upsampler, which modulates the pixel
features in discrete space via a periodic nonlinear function to generate
features for continuous pixels. To enhance the extracted features, we further
propose an enhanced transformer as the feature extraction backbone, where
convolution and attention branches are utilized parallelly. Besides, we
construct a large scale SCI2K dataset to facilitate the research on SCI SR.
Experimental results on nine datasets demonstrate that the proposed method
achieves state-of-the-art performance for SCI SR (outperforming SwinIR by 0.74
dB for x3 SR) and also works well for natural image SR. Our codes and dataset
will be released upon the acceptance of this work.
