CN112308803B - 一种基于深度学习的自监督低照度图像增强及去噪方法 - Google Patents
一种基于深度学习的自监督低照度图像增强及去噪方法 Download PDFInfo
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CN112907570B (zh) * | 2021-03-24 | 2022-03-22 | 合肥工业大学 | 一种轻量级无监督暗光图像增强方法及装置 |
CN113112484B (zh) * | 2021-04-19 | 2021-12-31 | 山东省人工智能研究院 | 一种基于特征压缩和噪声抑制的心室图像分割方法 |
CN113592733A (zh) * | 2021-07-22 | 2021-11-02 | 北京小米移动软件有限公司 | 图像处理方法、装置、存储介质及电子设备 |
CN114004761B (zh) * | 2021-10-29 | 2024-07-02 | 福州大学 | 一种融合深度学习夜视增强与滤波降噪的图像优化方法 |
CN114782418B (zh) * | 2022-06-16 | 2022-09-16 | 深圳市信润富联数字科技有限公司 | 瓷砖表面缺陷的检测方法及装置、存储介质 |
CN116363009B (zh) * | 2023-03-31 | 2024-03-12 | 哈尔滨工业大学 | 基于有监督学习的快速轻量化低照度图像增强方法及*** |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103903229A (zh) * | 2014-03-13 | 2014-07-02 | 中安消技术有限公司 | 一种夜晚图像增强方法和装置 |
CN106846282A (zh) * | 2017-03-28 | 2017-06-13 | 华侨大学 | 一种采用自适应校正的低照度图像增强方法 |
CN110163818A (zh) * | 2019-04-28 | 2019-08-23 | 武汉理工大学 | 一种用于海事无人机的低照度视频图像增强方法 |
CN110675336A (zh) * | 2019-08-29 | 2020-01-10 | 苏州千视通视觉科技股份有限公司 | 一种低照度图像增强方法及装置 |
CN111402145A (zh) * | 2020-02-17 | 2020-07-10 | 哈尔滨工业大学 | 一种基于深度学习的自监督低照度图像增强方法 |
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JP5743384B2 (ja) * | 2009-04-14 | 2015-07-01 | キヤノン株式会社 | 画像処理装置及び画像処理方法とコンピュータプログラム |
US9460497B2 (en) * | 2012-09-21 | 2016-10-04 | Htc Corporation | Image enhancement methods and systems using the same |
US20180211121A1 (en) * | 2017-01-25 | 2018-07-26 | Ford Global Technologies, Llc | Detecting Vehicles In Low Light Conditions |
CN107527332B (zh) * | 2017-10-12 | 2020-07-31 | 长春理工大学 | 基于改进Retinex的低照度图像色彩保持增强方法 |
CA2995708C (en) * | 2018-02-20 | 2021-11-02 | Synaptive Medical (Barbados) Inc. | System and method for performing local-area contrast enhancement of digital images |
CN109712097B (zh) * | 2019-01-04 | 2021-04-30 | Oppo广东移动通信有限公司 | 图像处理方法、装置、存储介质及电子设备 |
CN111489303A (zh) * | 2020-03-27 | 2020-08-04 | 武汉理工大学 | 一种低照度环境下海事图像增强方法 |
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Publication number | Priority date | Publication date | Assignee | Title |
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CN103903229A (zh) * | 2014-03-13 | 2014-07-02 | 中安消技术有限公司 | 一种夜晚图像增强方法和装置 |
CN106846282A (zh) * | 2017-03-28 | 2017-06-13 | 华侨大学 | 一种采用自适应校正的低照度图像增强方法 |
CN110163818A (zh) * | 2019-04-28 | 2019-08-23 | 武汉理工大学 | 一种用于海事无人机的低照度视频图像增强方法 |
CN110675336A (zh) * | 2019-08-29 | 2020-01-10 | 苏州千视通视觉科技股份有限公司 | 一种低照度图像增强方法及装置 |
CN111402145A (zh) * | 2020-02-17 | 2020-07-10 | 哈尔滨工业大学 | 一种基于深度学习的自监督低照度图像增强方法 |
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