## 1 Introduction  
Single-image super-resolution (SISR) [1], a pivotal task in low-level computer vision, focuses on reconstructing high-resolution images from their degraded low-resolution counterparts by restoring missing texture details and improving visual fidelity. However, the inherent diversity in image styles and content often leads to varying degrees of information degradation during reconstruction, particularly in preserving and enhancing intricate spatial features. The proliferation of deep learning techniques has driven significant advancements in this domain, with numerous advanced SR approaches being developed to address these reconstruction challenges. Convolutional neural networks (CNNs) [2; 3; 4; 5; 6; 7; 8; 9; 10] are the earliest deep learning architectures widely adopted in image processing. The CNN-based method has the characteristics of inductive bias, including localized receptive fields, translation invariance, and hierarchical spatial representations. It can achieve robust performance even with limited training data. CNN-based SISR models excel in capturing local features through these intrinsic properties and weight-sharing mechanisms, thereby notably improving texture reconstruction in degraded images. Despite these strengths, they also inherit the limitation of CNNs in capturing long-range dependencies, resulting in insufficient modeling of distant feature relationships and inability to better enhance the overall image details. Transformer [11] is celebrated for its robust capacity to model long-range dependencies and has progressively emerged as a prominent research focus in computer vision. Transformer-based SISR models [12; 13; 14; 15; 16] address the inherent limitation of CNNs in capturing global dependencies by leveraging the self-attention mechanism to capture global dependencies and model interactions across distant spatial positions. However, the quadratic computational complexity O(N\({}^{2}\)) associated with the self-attention mechanism necessitates considerably higher computational resources, particularly hindering the processing of high-resolution images. To mitigate this challenge, researchers have introduced a window-based self-attention mechanism [17]. This approach divides the input image into multiple local windows for parallel computation to alleviate the complexity, while the incorporation of sliding window attention further augments the global modeling capacity. Despite the fact that sliding window strategies further enhance inter-window communication, it remains insufficient for comprehensively modeling long-range dependencies, ultimately detracting from the quality of image reconstruction, especially in preserving fine-grained structural coherence. To overcome these challenges, researchers have progressively investigated several innovative mechanisms, such as linear attention [18] and Mambo [19]. Linear attention reformulates traditional self-attention by linearly decomposing the dot-product operations between query (Q) and key (K) features, effectively reducing computational complexity from O(N\({}^{2}\)) to O(N). While this approach mitigates computational overhead, it compromises the model's capacity to capture comprehensive global contextual dependencies, resulting in unstable performance across diverse tasks and data distributions. Mambo, an architecture derived from state space models [20; 21; 22], integrates the complementary strengths of recurrent neural networks [23; 24] and convolutional neural networks. This hybridization preserves its ability to model long-range dependencies while achieving substantial computational efficiency gains. However, its multi-directional scanning strategy is limited in non-autoregressive tasks, and issues such as local pixel forgetting and channel redundancy impede its pixel-level accuracy in image super-resolution.