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In this paper, we address this issue by\nproposing an accurate and lightweight deep network for image super-resolution.\nIn detail, we design an architecture that implements a cascading mechanism upon\na residual network. We also present variant models of the proposed cascading\nresidual network to further improve efficiency. Our extensive experiments show\nthat even with much fewer parameters and operations, our models achieve\nperformance comparable to that of state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1803.08664v5","url_pdf":"http://arxiv.org/pdf/1803.08664v5.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":"fast-accurate-and-lightweight-super-1","repo_url":"https://github.com/coloquinte/torchsr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fast-accurate-and-lightweight-super-1","repo_url":"https://github.com/godpgf/scarn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fast-accurate-and-lightweight-super-1","repo_url":"https://github.com/nmhkahn/CARN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-bsd100-2x-upscaling","task":"Image Super-Resolution","dataset":"BSD100 - 2x upscaling","model":"CARN [[Ahn et al.2018]]","rank_in_archive_order":24,"of":30,"metrics":{"PSNR":"32.09"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-bsd100-4x-upscaling","task":"Image Super-Resolution","dataset":"BSD100 - 4x upscaling","model":"CARN","rank_in_archive_order":32,"of":71,"metrics":{"PSNR":"27.58","SSIM":"0.7349"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-manga109-4x","task":"Image Super-Resolution","dataset":"Manga109 - 4x upscaling","model":"CARN","rank_in_archive_order":39,"of":50,"metrics":{"PSNR":"30.40","SSIM":"0.9082"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-2x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 2x upscaling","model":"CARN [[Ahn et al.2018]]","rank_in_archive_order":27,"of":35,"metrics":{"PSNR":"33.52"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-2x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 2x upscaling","model":"CARN-M [[Ahn et al.2018]]","rank_in_archive_order":28,"of":35,"metrics":{"PSNR":"33.26"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"CARN","rank_in_archive_order":57,"of":104,"metrics":{"PSNR":"28.60","SSIM":"0.7806"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set5-2x-upscaling","task":"Image Super-Resolution","dataset":"Set5 - 2x upscaling","model":"CARN [[Ahn et al.2018]]","rank_in_archive_order":30,"of":41,"metrics":{"PSNR":"37.76"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-urban100-4x","task":"Image Super-Resolution","dataset":"Urban100 - 4x upscaling","model":"CARN","rank_in_archive_order":42,"of":65,"metrics":{"PSNR":"26.07","SSIM":"0.7837"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08664"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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