看见更多细节：通过专家挖掘实现高效图像超分辨率
See More Details: Efficient Image Super-Resolution by Experts Mining
AI总结：
本文提出SeemoRe高效图像超分辨率模型，通过在不同层级引入秩维与空间维专家并采用低秩专家混合进行专家挖掘，以最小计算成本实现最优重建性能。
摘要：
从低分辨率（LR）输入重建高分辨率（HR）图像是图像超分辨率（SR）中的一个重大挑战。尽管近期方法已证明针对不同目标定制的复杂操作的有效性，但直接堆叠这些不同操作会导致巨大的计算负担，妨碍其实际应用。为此，我们提出SeemoRe，一种采用专家挖掘的高效SR模型。我们的方法在不同层级策略性地引入专家，采用协作式方法论。在宏观尺度上，我们的专家处理秩维和空间维的信息性特征，提供整体性理解。随后，模型通过利用低秩专家混合深入探究秩选择的细微之处。通过利用专门针对精确SR至关重要的不同关键因素的专家，我们的模型擅长揭示特征内部的复杂细节。这种协作方式类似于“看见更多”的概念，使我们的模型能够在高效设置下以最小的计算成本实现最优性能。源代码将在https://github.com/eduardzamfir/seemoredetails公开发布。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
