Lightweight neural networks for single-image super-resolution (SISR) tasks have made substantial breakthroughs in recent years. Compared to low-frequency information, high-frequency detail is much more difficult to reconstruct. Most SISR models allocate equal computational resources for low-frequency and high-frequency information, which leads to redundant processing of simple low-frequency information and inadequate recovery of more challenging high-frequency information. We propose a novel High-Frequency Focused Network (HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance high-frequency information while minimizing redundant feature computation of low-frequency information. The HFFB effectively allocates more computational resources to the more challenging reconstruction of high-frequency information. Moreover, we propose a Local Feature Fusion Block (LFFB) effectively fuses features from multiple HFFBs in a local region, utilizing complementary information across layers to enhance feature representativeness and reduce artifacts in reconstructed images. We assess the efficacy of our proposed HFFN on five benchmark datasets and show that it significantly enhances the super-resolution performance of the network. Our experimental results demonstrate state-of-the-art performance in reconstructing high-frequency information while using a low number of parameters.

翻译：轻量级神经网络在单图像超分辨率（SISR）任务中近年来取得了重大突破。与低频信息相比，高频细节的重建难度要大得多。大多数SISR模型对低频和高频信息分配相同的计算资源，这导致对简单低频信息的冗余处理以及对更具挑战性的高频信息恢复不足。我们提出了一种新型的高频聚焦网络（HFFN），该网络通过高频聚焦模块（HFFBs）选择性地增强高频信息，同时减少对低频信息的冗余特征计算。HFFB有效将更多计算资源分配给更具挑战性的高频信息重建。此外，我们提出了一种局部特征融合模块（LFFB），该模块有效融合局部区域内多个HFFB的特征，利用跨层的互补信息增强特征表征能力并减少重建图像中的伪影。我们在五个基准数据集上评估了所提出的HFFN的有效性，结果表明它能显著提升网络的超分辨率性能。我们的实验证明，在保持低参数量的同时，该方法在高频信息重建方面达到了最先进的性能。