Knowledge Commons of Institute of Automation,CAS
BLIND DENOISING OF FLUORESCENCE MICROSCOPY IMAGES USING GAN-BASED GLOBAL NOISE MODELING | |
Liqun Zhong1,2; Guole Liu1,2; Ge Yang1,2 | |
2021 | |
会议名称 | 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) |
会议日期 | April 13-16, 2021 |
会议地点 | Nice, France |
摘要 | Fluorescence microscopy is a key driving force behind advances in modern life sciences. However, due to constraints in image formation and acquisition, to obtain high signal-to-noise ratio (SNR) fluorescence images remains difficult. Strong noise negatively affects not only visual observation but also downstream analysis. To address this problem, we propose a blind global noise modeling denoiser (GNMD) that simulates image noise globally using a generative adversarial network (GAN). No prior information on noise properties is required. And no clean training targets need to be provided for noisy inputs. Instead, by simulating real image noise using a GAN, our method synthesizes paired noisy and clean images for training a denoising deep learning network. Experiments on real fluorescence microscopy images show that our method substantially outperforms competing state-of-the-art methods, especially in suppressing background noise. Denoising using our method also facilitates downstream image segmentation. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57366 |
专题 | 多模态人工智能系统全国重点实验室_计算生物学与机器智能 |
通讯作者 | Ge Yang |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liqun Zhong,Guole Liu,Ge Yang. BLIND DENOISING OF FLUORESCENCE MICROSCOPY IMAGES USING GAN-BASED GLOBAL NOISE MODELING[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
【ISBI 2021】Blind Den(1703KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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