See More Details: Efficient Image Super-Resolution by Experts Mining
Eduard Zamfir Zongwei Wu Nancy Mehta Yulun Zhang Radu Timofte
Reconstructing high-resolution (HR) images from low-resolution (LR) inputs
poses a significant challenge in image super-resolution (SR). While recent
approaches have demonstrated the efficacy of intricate operations customized
for various objectives, the straightforward stacking of these disparate
operations can result in a substantial computational burden, hampering their
practical utility. In response, we introduce SeemoRe, an efficient SR model
employing expert mining. Our approach strategically incorporates experts at
different levels, adopting a collaborative methodology. At the macro scale, our
experts address rank-wise and spatial-wise informative features, providing a
holistic understanding. Subsequently, the model delves into the subtleties of
rank choice by leveraging a mixture of low-rank experts. By tapping into
experts specialized in distinct key factors crucial for accurate SR, our model
excels in uncovering intricate intra-feature details. This collaborative
approach is reminiscent of the concept of "see more", allowing our model to
achieve an optimal performance with minimal computational costs in efficient
settings. The source will be publicly made available at
https://github.com/eduardzamfir/seemoredetails