GhostSR: Learning Ghost Features for Efficient Image Super-Resolution. (arXiv:2101.08525v2 [eess.IV] UPDATED)

Modern single image super-resolution (SISR) system based on convolutional
neural networks (CNNs) achieves fancy performance while requires huge
computational costs. The problem on feature redundancy is well studied in
visual recognition task, but rarely discussed in SISR. Based on the observation
that many features in SISR models are also similar to each other, we propose to
use shift operation to generate the redundant features (i.e., ghost features).
Compared with depth-wise convolution which is time-consuming on GPU-like
devices, shift operation can …

arxiv features ghost image learning