论文标题
具有特征相互作用加权混合网络的有效图像超分辨率
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
论文作者
论文摘要
轻量级图像超分辨率旨在使用低计算成本从低分辨率图像中重建高分辨率图像。但是，现有方法导致由于激活功能而导致中层特征的丧失。为了最大程度地减少中间特征损失对重建质量的影响，我们提出了一个特征相互作用加权混合网络（FIWHN），该网络包括一系列广泛的弥撒蒸馏交互块（WDIB）作为骨干。每三个WDIB都会通过应用相互的信息洗牌和融合来形成特征洗牌加权组（FSWG）。此外，为了减轻中间特征损失的负面影响，我们在WDIB中引入了广泛的残留加权单元。这些单元通过广泛的蒸馏连接（WRDC）和自校准融合（SCF）有效地融合了各种细节的特征。为了弥补全球特征缺陷，我们结合了一个变压器，并探索了一个新颖的体系结构，以结合CNN和变压器。我们表明，通过对低级和高级任务的广泛实验，我们的FIWHN在绩效和效率之间取得了良好的平衡。代码将在\ url {https://github.com/iviplab/fiwhn}上找到。
Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at \url{https://github.com/IVIPLab/FIWHN}.
