  Pre-training has marked numerous state of the arts in high-level computer
vision, while few attempts have ever been made to investigate how pre-training
acts in image processing systems. In this paper, we tailor transformer-based
pre-training regimes that boost various low-level tasks. To comprehensively
diagnose the influence of pre-training, we design a whole set of principled
evaluation tools that uncover its effects on internal representations. The
observations demonstrate that pre-training plays strikingly different roles in
low-level tasks. For example, pre-training introduces more local information to
higher layers in super-resolution (SR), yielding significant performance gains,
while pre-training hardly affects internal feature representations in
denoising, resulting in limited gains. Further, we explore different methods of
pre-training, revealing that multi-related-task pre-training is more effective
and data-efficient than other alternatives. Finally, we extend our study to
varying data scales and model sizes, as well as comparisons between
transformers and CNNs-based architectures. Based on the study, we successfully
develop state-of-the-art models for multiple low-level tasks. Code is released
at https://github.com/fenglinglwb/EDT.
