  Recently, lots of deep networks are proposed to improve the quality of
predicted super-resolution (SR) images, due to its widespread use in several
image-based fields. However, with these networks being constructed deeper and
deeper, they also cost much longer time for training, which may guide the
learners to local optimization. To tackle this problem, this paper designs a
training strategy named Pixel-level Self-Paced Learning (PSPL) to accelerate
the convergence velocity of SISR models. PSPL imitating self-paced learning
gives each pixel in the predicted SR image and its corresponding pixel in
ground truth an attention weight, to guide the model to a better region in
parameter space. Extensive experiments proved that PSPL could speed up the
training of SISR models, and prompt several existing models to obtain new
better results. Furthermore, the source code is available at
https://github.com/Elin24/PSPL.
