计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 164-168.doi: 10.11896/jsjkx.200100099
所属专题: 医学图像
王伟, 胡涛, 李欣蔚, 沈思婉, 姜小明, 刘峻源
WANG Wei, HU Tao, LI Xin-wei, SHEN Si-wan, JIANG Xiao-ming, LIU Jun-yuan
摘要: 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更清晰的目的。所提出的方法在现有生成对抗网络的超分辨率方法(Super-Resolution Generative Adversarial Network,SRGAN) 的基础上,设计引入嵌套型残差密集块(Residual-in-Residual Dense Block,RRDB)来改进网络结构,并对原有标准残差块中的批量规范化层进行删减,以提升网络性能,另外还对判别器的损失函数进行了改进。实验结果表明,该方法(SRGAN+)与3种插值法以及4种基于学习的超分辨率方法相比,在提高分辨率的同时,获得了图片细节更丰富、人眼视觉更优的图像。与SRGAN方法相比,改进算法在峰值信噪比(Peak Signal-to-noise Ratio,PSNR)和结构相似度(Structural SIMilarity,SSIM)上分别有1.008 dB和1.07%的提高。
中图分类号:
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