Convolutional Neural Networks (CNNs) have been widely employed for image Super-Resolution (SR) in recent years. Various techniques enhance SR performance by altering CNN structures or incorporating improved self-attention mechanisms. Interestingly, these advancements share a common trait. Instead of explicitly learning high-frequency details, they learn an implicit feature processing mode that utilizes weighted sums of a feature map's own elements for reconstruction, akin to convolution and non-local. In contrast, early dictionary-based approaches learn feature decompositions explicitly to match and rebuild Low-Resolution (LR) features. Building on this analysis, we introduce Trainable Feature Matching (TFM) to amalgamate this explicit feature learning into CNNs, augmenting their representation capabilities. Within TFM, trainable feature sets are integrated to explicitly learn features from training images through feature matching. Furthermore, we integrate non-local and channel attention into our proposed Trainable Feature Matching Attention Network (TFMAN) to further enhance SR performance. To alleviate the computational demands of non-local operations, we propose a streamlined variant called Same-size-divided Region-level Non-Local (SRNL). SRNL conducts non-local computations in parallel on blocks uniformly divided from the input feature map. The efficacy of TFM and SRNL is validated through ablation studies and module explorations. We employ a recurrent convolutional network as the backbone of our TFMAN to optimize parameter utilization. Comprehensive experiments on benchmark datasets demonstrate that TFMAN achieves superior results in most comparisons while using fewer parameters. The code is available at https://github.com/qizhou000/tfman.

翻译：近年来，卷积神经网络（CNNs）已被广泛应用于图像超分辨率（SR）任务。多种技术通过改变CNN结构或引入改进的自注意力机制来提升SR性能。有趣的是，这些进展具有一个共同特点：它们并非显式地学习高频细节，而是学习一种隐式的特征处理模式，利用特征图自身元素的加权和进行重建，类似于卷积与非局部操作。相比之下，早期的基于字典的方法显式地学习特征分解以匹配并重建低分辨率（LR）特征。基于此分析，我们引入可训练特征匹配（TFM），将这种显式特征学习融入CNNs，以增强其表示能力。在TFM中，通过集成可训练特征集，经由特征匹配从训练图像中显式学习特征。此外，我们将非局部注意力与通道注意力整合到所提出的可训练特征匹配注意力网络（TFMAN）中，以进一步提升SR性能。为减轻非局部操作的计算负担，我们提出一种简化变体，称为等分区域级非局部注意力（SRNL）。SRNL在从输入特征图均匀划分的块上并行执行非局部计算。通过消融实验与模块探索，验证了TFM与SRNL的有效性。我们采用循环卷积网络作为TFMAN的主干，以优化参数利用率。在基准数据集上的综合实验表明，TFMAN在多数对比中取得了更优的结果，同时使用了更少的参数。代码发布于https://github.com/qizhou000/tfman。