Wei Yangjie, Li Qifei, Hou Weihan
Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, College of Computer Science and Engineering, Northeastern University, Wenhua Street 3, Shenyang, 110819, China.
Heliyon. 2024 Aug 10;10(16):e36151. doi: 10.1016/j.heliyon.2024.e36151. eCollection 2024 Aug 30.
Defocus blurring imaging seriously affects the observation accuracy and application range of optical microscopes, and the blurring kernel function is a key parameter for high-resolution image restoration. However, its solving process is complicated and high in computational cost. Image restoration based on most neural networks has high requirements on data sets and the image resolution after restoration is limited because of the lack of quantitative estimation of blurring kernels. In this study, an image restoration method guided by blurring kernel estimation for microscopic defocused images is proposed. First, to reduce the blurring kernel estimation error caused by the positive and negative difference in microscopic defocused imaging, a defocused image classification network is designed to classify the input defocused images with different defocus distances and directions, and its output images are input into the blurring kernel extraction network composed of the feature extraction, correlation, and blurring kernel reconstruction layers. Second, a non-blind defocused image restoration model to restore the high-resolution images is proposed by introducing the blurring kernel extraction module into the restoration network based on U-Net, and the blurring kernel estimation and image restoration losses are jointly trained to realize image restoration guided by blurring kernel estimation. Finally, the experimental results of our proposed method demonstrate significant improvements in both the peak signal-to-noise ratio and structural similarity index measure when compared to other methods.
散焦模糊成像严重影响光学显微镜的观察精度和应用范围，模糊核函数是高分辨率图像复原的关键参数。然而，其求解过程复杂且计算成本高。基于大多数神经网络的图像复原对数据集有很高要求，并且由于缺乏对模糊核的定量估计，复原后的图像分辨率有限。在本研究中，提出了一种基于模糊核估计的显微散焦图像复原方法。首先，为了减少显微散焦成像中正负差异引起的模糊核估计误差，设计了一个散焦图像分类网络，对具有不同散焦距离和方向的输入散焦图像进行分类，并将其输出图像输入到由特征提取、相关性和模糊核重建层组成的模糊核提取网络中。其次，通过将模糊核提取模块引入基于U-Net的复原网络中，提出了一种非盲散焦图像复原模型来复原高分辨率图像，并联合训练模糊核估计和图像复原损失，以实现基于模糊核估计的图像复原。最后，与其他方法相比，我们提出的方法的实验结果表明，在峰值信噪比和结构相似性指数测量方面都有显著提高。
