{"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/deeplsr-a-deep-learning-approach-for-laser","title":"DeepLSR: a deep learning approach for laser speckle reduction","arxiv_id":"1810.10039","date":"2018-10-23","proceeding":null,"authors":["Taylor L. Bobrow","Faisal Mahmood","Miguel Inserni","Nicholas J. Durr"],"abstract":"Speckle artifacts degrade image quality in virtually all modalities that\nutilize coherent energy, including optical coherence tomography, reflectance\nconfocal microscopy, ultrasound, and widefield imaging with laser illumination.\nWe present an adversarial deep learning framework for laser speckle reduction,\ncalled DeepLSR (https://durr.jhu.edu/DeepLSR), that transforms images from a\nsource domain of coherent illumination to a target domain of speckle-free,\nincoherent illumination. We apply this method to widefield images of objects\nand tissues illuminated with a multi-wavelength laser, using light emitting\ndiode-illuminated images as ground truth. In images of gastrointestinal\ntissues, DeepLSR reduces laser speckle noise by 6.4 dB, compared to a 2.9 dB\nreduction from optimized non-local means processing, a 3.0 dB reduction from\nBM3D, and a 3.7 dB reduction from an optical speckle reducer utilizing an\noscillating diffuser. Further, DeepLSR can be combined with optical speckle\nreduction to reduce speckle noise by 9.4 dB. This dramatic reduction in speckle\nnoise may enable the use of coherent light sources in applications that require\nsmall illumination sources and high-quality imaging, including medical\nendoscopy.","url_abs":"http://arxiv.org/abs/1810.10039v4","url_pdf":"http://arxiv.org/pdf/1810.10039v4.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":"deeplsr-a-deep-learning-approach-for-laser","repo_url":"https://github.com/faisalml/DeepLSR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deeplsr-a-deep-learning-approach-for-laser","repo_url":"https://github.com/mahmoodlab/DeepLSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}