HSNet: Heterogeneous Subgraph Network for Single Image Super-resolution

• URL: http://arxiv.org/abs/2510.06564v1
• Date: Wed, 08 Oct 2025 01:32:52 GMT
• Title: HSNet: Heterogeneous Subgraph Network for Single Image Super-resolution
• Authors: Qiongyang Hu, Wenyang Liu, Wenbin Zou, Yuejiao Su, Lap-Pui Chau, Yi Wang,
• Abstract summary: The Heterogeneous Subgraph Network (HSNet) is a novel framework that efficiently leverages graph modeling while maintaining computational feasibility.<n>HSNet achieves state-of-the-art performance, effectively balancing reconstruction quality with computational efficiency.
• Score: 27.18780594293798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing deep learning approaches for image super-resolution, particularly those based on CNNs and attention mechanisms, often suffer from structural inflexibility. Although graph-based methods offer greater representational adaptability, they are frequently impeded by excessive computational complexity. To overcome these limitations, this paper proposes the Heterogeneous Subgraph Network (HSNet), a novel framework that efficiently leverages graph modeling while maintaining computational feasibility. The core idea of HSNet is to decompose the global graph into manageable sub-components. First, we introduce the Constructive Subgraph Set Block (CSSB), which generates a diverse set of complementary subgraphs. Rather than relying on a single monolithic graph, CSSB captures heterogeneous characteristics of the image by modeling different relational patterns and feature interactions, producing a rich ensemble of both local and global graph structures. Subsequently, the Subgraph Aggregation Block (SAB) integrates the representations embedded across these subgraphs. Through adaptive weighting and fusion of multi-graph features, SAB constructs a comprehensive and discriminative representation that captures intricate interdependencies. Furthermore, a Node Sampling Strategy (NSS) is designed to selectively retain the most salient features, thereby enhancing accuracy while reducing computational overhead. Extensive experiments demonstrate that HSNet achieves state-of-the-art performance, effectively balancing reconstruction quality with computational efficiency. The code will be made publicly available.

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