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However, existing methods\noften require a large number of network parameters and entail heavy\ncomputational loads at runtime for generating high-accuracy super-resolution\nresults. In this paper, we propose the deep Laplacian Pyramid Super-Resolution\nNetwork for fast and accurate image super-resolution. The proposed network\nprogressively reconstructs the sub-band residuals of high-resolution images at\nmultiple pyramid levels. In contrast to existing methods that involve the\nbicubic interpolation for pre-processing (which results in large feature maps),\nthe proposed method directly extracts features from the low-resolution input\nspace and thereby entails low computational loads. We train the proposed\nnetwork with deep supervision using the robust Charbonnier loss functions and\nachieve high-quality image reconstruction. Furthermore, we utilize the\nrecursive layers to share parameters across as well as within pyramid levels,\nand thus drastically reduce the number of parameters. Extensive quantitative\nand qualitative evaluations on benchmark datasets show that the proposed\nalgorithm performs favorably against the state-of-the-art methods in terms of\nrun-time and image quality.","url_abs":"http://arxiv.org/abs/1710.01992v3","url_pdf":"http://arxiv.org/pdf/1710.01992v3.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-and-accurate-image-super-resolution-with","repo_url":"https://github.com/Araxeus/PNG-Upscale","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/dokyeongK/Single-Image-Super-Resolution-Reimplemenation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/fannymonori/TF-LapSRN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/nhatsmrt/superres","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/poikilos/pyrotocanvas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/pratik-kubal/Deep-Laplacian-Pyramid-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fast-and-accurate-image-super-resolution-with","repo_url":"https://github.com/lorenzomammana/MSLapSRN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.01992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.01992"}},"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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