Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input. Transformer-based methods have achieved remarkable performance by modeling long-range dependencies in degraded images. However, their feature-intensive attention computation incurs high computational cost. To improve efficiency, most existing approaches partition images into fixed groups and restrict attention within each group. Such group-wise attention overlooks the inherent asymmetry in token similarities, thereby failing to enable flexible and token-adaptive attention computation. To address this limitation, we propose the Individualized Exploratory Transformer (IET), which introduces a novel Individualized Exploratory Attention (IEA) mechanism that allows each token to adaptively select its own content-aware and independent attention candidates. This token-adaptive and asymmetric design enables more precise information aggregation while maintaining computational efficiency. Extensive experiments on standard SR benchmarks demonstrate that IET achieves state-of-the-art performance under comparable computational complexity.

翻译：单图像超分辨率（SISR）是一项旨在从低分辨率输入重建高分辨率图像的基础计算机视觉任务。基于Transformer的方法通过建模退化图像中的长程依赖关系取得了显著性能，但其特征密集型注意力计算带来了高昂的计算成本。为提高效率，现有方法大多将图像划分为固定组别并将注意力限制在各组内部。此类分组注意力机制忽视了标记相似性固有的非对称性，因而无法实现灵活且标记自适应的注意力计算。为突破这一局限，我们提出个体化探索Transformer（IET），其引入的新型个体化探索注意力（IEA）机制允许每个标记自适应地选择其自身内容感知且独立的注意力候选集。这种标记自适应与非对称的设计在保持计算效率的同时实现了更精确的信息聚合。在标准超分辨率基准上的大量实验表明，IET在可比较的计算复杂度下达到了最先进的性能水平。