Abstract
In recent years, the performance of lightweight Single-Image Super-Resolution (SISR) has been improved significantly with the application of Convolutional Neural Networks (CNNs) and Large Kernel Attention (LKA). However, existing information distillation modules for lightweight SISR struggle to map inputs into High-Dimensional Non-Linear (HDNL) feature spaces, limiting their representation learning. And their LKA modules possess restricted ability to capture the multi-shape multi-scale information for long-range dependencies while encountering a quadratic increase in the computational burden with increasing convolutional kernel size of its depth-wise convolutional layer. To address these issues, we firstly propose a Star Distillation Module (SDM) to enhance the discriminative representation learning via information distillation in the HDNL feature spaces. Besides, we present a Multi-shape Multi-scale Large Kernel Attention (MM-LKA) module to learn representative long-range dependencies while incurring low computational and memory footprints, leading to improving the performance of CNN-based self-attention significantly. Integrating SDM and MM-LKA, we develop a Residual Star Distillation Attention Module (RSDAM) and take it as the building block of the proposed efficient Star Distillation Attention Network (SDAN) which possesses high reconstruction efficiency to recover a higher-quality image from the corresponding low-resolution (LR) counterpart. When compared with other lightweight state-of-the-art SISR methods, extensive experiments show that our SDAN with low model complexity yields superior performance quantitatively and visually.
Abstract (translated)
近年来，通过应用卷积神经网络（CNN）和大型核注意力机制（LKA），轻量级单图像超分辨率（SISR）的性能得到了显著提升。然而，现有的信息蒸馏模块在处理轻量级 SISR 时难以将输入映射到高维非线性（HDNL）特征空间中，这限制了它们的学习表示能力。此外，这些 LKA 模块在捕捉长距离依赖关系中的多形状和多尺度信息方面表现出受限的能力，并且随着其深度卷积层的核大小增加，计算负担呈二次增长。 为解决这些问题，我们首先提出了一个星形蒸馏模块（SDM），通过在 HDNL 特征空间中进行信息蒸馏来增强辨别式表示学习。此外，我们提出了一种多形状多尺度大型核注意力机制（MM-LKA）模块，在保持低计算和内存开销的同时，能够有效学习长距离依赖关系，并显著提升了基于 CNN 的自注意力性能。 通过整合 SDM 和 MM-LKA 模块，我们开发了一个残差星形蒸馏注意模块（RSDAM），并将其作为所提出的高效星形蒸馏注意网络（SDAN）的构建模块。该网络具有高效的重建能力，可以从对应的低分辨率图像中恢复出高质量的高分辨率图像。 与现有的轻量级 SISR 方法相比，大量实验表明，在模型复杂度较低的情况下，我们的 SDAN 网络在量化和视觉效果上均表现出色。
URL
https://arxiv.org/abs/2506.12475
