## 1 Introduction
The Mambo-like linear attention (MLLA) model [25] addresses these limitations by establishing a theoretical bridge between Mambo and linear attention, effectively combining the parallel processing efficiency of linear attention with the adaptive feature selection capability of SSM. Operating at linear complexity O(N), MLLA selectively prioritizes critical detail features during reconstruction. Crucially, it circumvents the recursive computation demands of SSM's forget gates, thereby eliminating a key bottleneck in high-resolution image processing. This framework represents a paradigm shift, balancing computational tractability with enhanced reconstruction precision. In order to solve the problem of image artifacts and contour blurring in the high-frequency detail reconstruction of existing lightweight methods in complex degraded scenes, this study proposes a novel hybrid architecture lightweight SR network designed to enhance texture detail reconstruction while maintaining a low parameter count. The Dense Residual Fused Attention Network (DRFAN) integrates the Mamba-like linear attention (MLLA) module and Grid Attention Block (GAB) to synergize global contextual modeling with localized feature refinement. Furthermore, it employs dense residual connections to mitigate spatial information loss and channel redundancy in deep networks. Compared with mainstream lightweight models, the DRFAN achieves substantial improvements in reconstructing fine-grained details under constrained parameter budgets. The primary contributions of this paper are as follows:  
1. We propose the DRFAN, a novel lightweight SISR framework that innovatively integrates the MLLA module and GAB into a unified hybrid architecture. This design enables dynamic multi-scale feature selection through parallel processing mechanisms, reduces channel redundancy, and establishes cross-layer global context modeling pathways, ensuring robust global representation capabilities. 2. We introduce the GAB module to extend pixel interaction beyond local regions via a hierarchical interaction strategy. By leveraging structural similarity priors between image patches, it optimizes spatial information aggregation efficiency, significantly enhancing the reconstruction of high-frequency textures and fine-grained details. 3. We design a tightly coupled residual framework to synergize the advantages of convolutional local feature extraction, the long-range dependency modeling capability of self-attention, and the efficient computational characteristics of Mamba. This complementary feature fusion mechanism achieves the coordinated optimization of these three core operations. 4. Experimental results on multiple benchmark SR datasets demonstrate that the DRFAN shows significant advantages in SISR tasks and industrial inspection scenarios, offering an efficient and reliable solution for practical applications. To address the limitations of existing lightweight super-resolution methods in reconstructing high-frequency textures under complex degradation, this study proposes several key innovations. First, we introduce the DRFAN, a hybrid architecture that tightly couples convolutional operations, Mamba-like linear attention, and a Grid Attention Block within a dense residual framework. Second, we replace traditional Multi-Layer Perception layers in attention modules with Spatial Gated Feedforward Networks (SGFNs), enhancing spatial modeling capacity and reducing channel redundancy. Third, through the complementary fusion of local, global, and sequential features, our design enables dynamic multi-scale feature selection with strong generalization capabilities. These innovations collectively advance both the theoretical framework and practical deployment feasibility for high-fidelity and efficient image super-resolution. The remainder of this paper is organized as follows: Section 2 reviews related works in lightweight and hybrid SR networks. Section 3 details the architecture of the proposed DRFAN model.