Li Wenhao, He Ding, Liu Yongqiang, Wang Fenghe, Huang Fengliang
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China.
Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control, School of Environment, Nanjing Normal University, Nanjing, China.
Front Plant Sci. 2023 Mar 21;14:1146485. doi: 10.3389/fpls.2023.1146485. eCollection 2023.
It is difficult for laser scanning confocal microscopy to obtain high- or ultra-high-resolution laser confocal images directly, which affects the deep mining and use of the embedded information in laser confocal images and forms a technical bottleneck in the in-depth exploration of the microscopic physiological and biochemical processes of plants. The super-resolution reconstruction model (SRGAN), which is based on a generative adversarial network and super-resolution reconstruction model (SRResNet), which is based on a residual network, was used to obtain single and secondary super-resolution reconstruction images of laser confocal images of the root cells of the hyperaccumulator . Using the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and mean opinion score (MOS), the models were evaluated by the image effects after reconstruction and were applied to the recognition of endocytic vesicles in nigrum root cells. The results showed that the single reconstruction and the secondary reconstruction of SRGAN and SRResNet improved the resolution of laser confocal images. PSNR, SSIM, and MOS were clearly improved, with a maximum PSNR of 47.690. The maximum increment of PSNR and SSIM of the secondary reconstruction images reached 21.7% and 2.8%, respectively, and the objective evaluation of the image quality was good. However, overall MOS was less than that of the single reconstruction, the perceptual quality was weakened, and the time cost was more than 130 times greater. The reconstruction effect of SRResNet was better than that of SRGAN. When SRGAN and SRResNet were used for the recognition of endocytic vesicles in root cells, the clarity of the reconstructed images was obviously improved, the boundary of the endocytic vesicles was clearer, and the number of identified endocytic vesicles increased from 6 to 9 and 10, respectively, and the mean fluorescence intensity was enhanced by 14.4% and 7.8%, respectively. Relevant research and achievements are of great significance for promoting the application of deep learning methods and image super-resolution reconstruction technology in laser confocal image studies.
激光扫描共聚焦显微镜难以直接获取高分辨率或超高分辨率的激光共聚焦图像，这影响了对激光共聚焦图像中所蕴含信息的深度挖掘与利用，成为植物微观生理生化过程深入探究中的技术瓶颈。基于生成对抗网络的超分辨率重建模型（SRGAN）以及基于残差网络的超分辨率重建模型（SRResNet）被用于获取超富集植物根细胞激光共聚焦图像的单次及二次超分辨率重建图像。利用峰值信噪比（PSNR）、结构相似性（SSIM）和平均意见得分（MOS），通过重建后的图像效果对模型进行评估，并将其应用于龙葵根细胞内吞小泡的识别。结果表明，SRGAN和SRResNet的单次重建和二次重建均提高了激光共聚焦图像的分辨率。PSNR、SSIM和MOS均有明显提升，PSNR最大值为47.690。二次重建图像的PSNR和SSIM最大增幅分别达到21.7%和2.8%，图像质量的客观评价良好。然而，整体MOS低于单次重建，感知质量有所减弱，且时间成本增加了130多倍。SRResNet的重建效果优于SRGAN。当使用SRGAN和SRResNet对龙葵根细胞内吞小泡进行识别时，重建图像的清晰度明显提高，内吞小泡的边界更清晰，识别出的内吞小泡数量分别从6个增加到9个和10个，平均荧光强度分别增强了14.4%和7.8%。相关研究成果对于推动深度学习方法和图像超分辨率重建技术在激光共聚焦图像研究中的应用具有重要意义。
