仿射变换
人工智能
过度拟合
降噪
计算机科学
图像配准
计算机视觉
深度学习
模式识别(心理学)
图像去噪
接头(建筑物)
噪音(视频)
人工神经网络
图像(数学)
数学
工程类
建筑工程
纯数学
作者
Kerem Celikay,Vadim O. Chagin,M. Cristina Cardoso,Karl Rohr
标识
DOI:10.1109/isbi52829.2022.9761507
摘要
Image registration is important for analysing time-lapse live cell microscopy images. However, this is challenging due to significant image noise and complex cell movement. We propose a novel end-to-end trainable deep neural network for joint denoising and affine registration of temporal live cell microscopy images. Our network is trained unsupervised, and only a single network is required for both tasks which reduces overfitting. Our experiments show that the proposed network performs better than deep affine registration without denoising, and better than sequential deep denoising and affine registration. In combination with deep non-rigid registration, we outperform state-of-the-art non-rigid registration methods.
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