原子力显微镜(AFM)是一种高精度的仪器,用于测量各种导电或非导电样品的表面形貌。然而,使用标准AFM扫描获得高分辨率图像需要更多时间。使用块压缩感知(BCS)是实现快速AFM成像的有效途径。但是,常规BCS-AFM成像很难平衡每个局部区域的图像质量。容易导致一些平坦区域采样过多,导致耗时。同时,在一些具有重要细节的领域缺乏抽样,导致成像质量差。因此,提出了一种创新的自适应BCS-AFM成像方法。重叠块用于消除块伪影。特征参数(GTV,卢,和SD)用于预测样品的局部形态特征。采用反向传播神经网络来获取每个子块的适当采样率。通过预扫描和自适应补充扫描获得采样点。之后,使用TVAL3算法重建所有子块图像。每个样品都能达到均匀,优良的图像质量。图像视觉效果和评价指标(PSNR和SSIM)用于评价和分析样品的成像效果。与两种非自适应和其他两种自适应成像方案相比,我们提出的方案具有自动化程度高的特点,均匀高质量的成像,和快速成像速度。摘要:提出的自适应BCS方法可以解决AFM中图像质量不均匀和成像速度慢的问题。通过BP神经网络可以获得样本的每个子块的适当采样率。GTV的介绍,卢,和SD可以有效地揭示AFM图像的形态特征。使用七个不同形态的样本来测试所提出的自适应算法的性能。用两个样本进行了实际实验,以验证所提出的自适应算法的可行性。
Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.