{"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/pynet-qxq-a-distilled-pynet-for-qxq-bayer","title":"PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image Sensors","arxiv_id":"2203.04314","date":"2022-03-08","proceeding":null,"authors":["Minhyeok Cho","Haechang Lee","Hyunwoo Je","Kijeong Kim","Dongil Ryu","Albert No"],"abstract":"Deep learning-based image signal processor (ISP) models for mobile cameras can generate high-quality images that rival those of professional DSLR cameras. However, their computational demands often make them unsuitable for mobile settings. Additionally, modern mobile cameras employ non-Bayer color filter arrays (CFA) such as Quad Bayer, Nona Bayer, and QxQ Bayer to enhance image quality, yet most existing deep learning-based ISP (or demosaicing) models focus primarily on standard Bayer CFAs. In this study, we present PyNET-QxQ, a lightweight demosaicing model specifically designed for QxQ Bayer CFA patterns, which is derived from the original PyNET. We also propose a knowledge distillation method called progressive distillation to train the reduced network more effectively. Consequently, PyNET-QxQ contains less than 2.5% of the parameters of the original PyNET while preserving its performance. Experiments using QxQ images captured by a proto type QxQ camera sensor show that PyNET-QxQ outperforms existing conventional algorithms in terms of texture and edge reconstruction, despite its significantly reduced parameter count.","url_abs":"https://arxiv.org/abs/2203.04314v2","url_pdf":"https://arxiv.org/pdf/2203.04314v2.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":"pynet-qxq-a-distilled-pynet-for-qxq-bayer","repo_url":"https://github.com/minhyeok01/pynet-qxq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"demosaicking","task_name":"Demosaicking"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.04314","atlas_url":"https://app.syntology.ai/?focus=2203.04314","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}