Wang Jianhang, Wang Xin, Zhang Ping, Xie Shiling, Fu Shujun, Li Yuliang, Han Hongbin
Opt Express. 2021 Aug 30;29(18):28503-28520. doi: 10.1364/OE.433064.
The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images. An effective fully convolutional network (FCN) is proposed to directly process color microscopic image in this paper. The proposed method estimates the distribution of illumination information in input image, and then carry out the correction of the corresponding uneven illumination through a feature encoder module, a feature decoder module, and a detail supplement module. In this process, overlapping residual blocks are designed to better transfer the illumination information, and in particular a well-designed weighted loss function ensures that the network can not only correct the illumination but also preserve image details. The proposed method is compared with some related methods on real pathological cell images qualitatively and quantitatively. Experimental results show that our method achieves the excellent performance. The proposed method is also applied to the preprocessing of whole slide imaging (WSI) tiles, which greatly improves the effect of image mosaicking.
微观图像中不均匀光照的校正乃是医学成像中的一项基础任务。现有的大多数方法都是针对单色图像设计的。本文提出了一种有效的全卷积网络（FCN）来直接处理彩色微观图像。所提方法估计输入图像中光照信息的分布，然后通过一个特征编码器模块、一个特征解码器模块和一个细节补充模块对相应的不均匀光照进行校正。在此过程中，设计了重叠残差块以更好地传递光照信息，特别是精心设计的加权损失函数确保网络不仅能够校正光照，还能保留图像细节。所提方法在真实病理细胞图像上与一些相关方法进行了定性和定量比较。实验结果表明我们的方法取得了优异的性能。所提方法还应用于全玻片成像（WSI）切片的预处理，极大地提高了图像拼接效果。
