白细胞图像超分辨率重建研究

近年来,计算机视觉已成为各类学科领域研究的重点,逐渐被应用于各类科研场景.医务工作者在临床上做血常规检验时,经常会采用血细胞图像分析系统对镜下白细胞图像进行自动计数与分类.其中,白细胞图像质量影响着血细胞分析系统计数分类的效果.针对镜下白细胞图像细节模糊的问题,文中尝试引入超分辨率方法对图片进行优化,以达到使白细胞图像...

Study on Super-resolution Image Reconstruction of Leukocytes

In recent years,computer vision has become the focus of research in various disciplines and has been gradually applied to numerous scientific research scenarios.Medical workers often use blood cell image analysis systems to automatically count and classify white blood cell images when performing blood routine tests in the clinic.Among them,the white blood cell image quality affects the counting classification effect of the blood cell analysis system.This paper focuses on the problem of blurred details of white blood cell images under the microscope and attempts to introduce a super-resolution method to solve the problem.This method introduces a Residual-in-Residual Dense Block(RRDB)based on the Super-Resolution Generative Adversarial Network(SRGAN)to improve the network structure and remove the batch normalization layer in the standard residual block.The network performance is improved and the loss function of the discriminator is improved.Experimental results show that,compared with 3 interpolation methods and 4 learning-based super-resolution methods,the proposed method(SRGAN+)improves the reso-lution while obtaining images with richer textures and more realistic visuals.Compared with the SRGAN method,the proposed algorithm has a 1.008 dB improvement in peak signal-to-noise ratio(PSNR)and 1.07%improvement in Structural SIMilarity(SSIM).