{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lightweight-image-super-resolution-with-4","title":"Lightweight Image Super-Resolution with Superpixel Token Interaction","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Aiping Zhang","Wenqi Ren","Yi Liu","Xiaochun Cao"],"abstract":"    Transformer-based methods have demonstrated impressive results on single-image super-resolution (SISR) task. However, self-attention mechanism is computationally expensive when applied to the entire image. As a result, current approaches divide low-resolution input images into small patches, which are processed separately and then fused to generate high-resolution images. Nevertheless, this conventional regular patch division is too coarse and lacks interpretability, resulting in artifacts and non-similar structure interference during attention operations. To address these challenges, we propose a novel super token interaction network (SPIN). Our method employs superpixels to cluster local similar pixels to form the explicable local regions and utilizes intra-superpixel attention to enable local information interaction. It is interpretable because only similar regions complement each other and dissimilar regions are excluded. Moreover, we design a superpixel cross-attention module to facilitate information propagation via the surrogation of superpixels. Extensive experiments demonstrate that the proposed SPIN model performs favorably against the state-of-the-art SR methods in terms of accuracy and lightweight. Code is available at https://github.com/ArcticHare105/SPIN.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Zhang_Lightweight_Image_Super-Resolution_with_Superpixel_Token_Interaction_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Zhang_Lightweight_Image_Super-Resolution_with_Superpixel_Token_Interaction_ICCV_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lightweight-image-super-resolution-with-4","repo_url":"https://github.com/arctichare105/spin","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"superpixels","task_name":"Superpixels"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"low-resolution-input","method_name":"Low-resolution input"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}