From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution

Single Image Super-Resolution (SISR) is a fundamental com-puter vision task that aims to reconstruct a high-resolution (HR) im-age from a low-resolution (LR) input. Transformer-based methods haveachieved remarkable performance by modeling long-range dependenciesin degraded images. However, their feature-intensive attention compu-tation incurs high computational cost. To improve efficiency, most ex-isting approaches partition images into fixed groups and restrict atten-tion within each group. Such group-wise attention overlooks the inher-ent asymmetry in token similarities, thereby failing to enable flexibleand token-adaptive attention computation. To address this limitation,we propose the Individualized Exploratory Transformer (IET), which in-troduces a novel Individualized Exploratory Attention (IEA) mechanismthat allows each token to adaptively select its own content-aware andindependent attention candidates. This token-adaptive and asymmetricdesign enables more precise information aggregation while maintainingcomputational efficiency. Extensive experiments on standard SR bench-marks demonstrate that IET achieves state-of-the-art performance undercomparable computational complexity. The code is available at here.