{"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/0-8-nyquist-computational-ghost-imaging-via","title":"0.8% Nyquist computational ghost imaging via non-experimental deep learning","arxiv_id":"2108.07673","date":"2021-08-17","proceeding":null,"authors":["Haotian Song","Xiaoyu Nie","Hairong Su","Hui Chen","Yu Zhou","Xingchen Zhao","Tao Peng","Marlan O. Scully"],"abstract":"We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.","url_abs":"https://arxiv.org/abs/2108.07673v1","url_pdf":"https://arxiv.org/pdf/2108.07673v1.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":[],"tasks":[{"task_slug":"3d-face-modeling","task_name":"3D Face Modelling"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"},{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"},{"task_slug":null,"task_name":"Overlapped 19-1"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":"TAP Air Portugal Travel Help via WhatsApp: 24/7 Assistance Made Easy"},{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":"Vishu"},{"method_slug":null,"method_name":null}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-domain-question-answering-on-natural","task":"Open-Domain Question Answering","dataset":"Natural Questions","model":"FiE","rank_in_archive_order":1,"of":5,"metrics":{"Exact Match":"58.4"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-natural-questions-long","task":"Question Answering","dataset":"Natural Questions (long)","model":"FiE","rank_in_archive_order":9,"of":13,"metrics":{"EM":"58.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}