Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution
Journal:
arXiv
Published Date:
Jun 14, 2025
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.
