The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

• algorithms applied are not part of the deep learning family of algorithms. The most widely used neural network type in image analysis is the convolutional neural network (CNN) [26][28][29][30][31]. CNN uses several manipulations to reduce the demand on computing resources and increase efficiency, as will be

• computation to circumvent difficulties in sufficient data acquisition is to train deep convolutional neural networks on simulated data. Deep STORM is a deep convolutional neural network that achieves high-resolution images from images recorded by a standard inverted microscope under bad imaging conditions

Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning. Keywords: atomic force microscopy; Bayesian compressed sensing; convolutional neural network; molecular dynamics simulation; super resolution; Introduction Atomic force microscopy methods

• addition to using the signal sparsity, learning-based super-resolution reconstruction methods are also focused issues and trends in research. The super-resolution convolutional neural network (SRCNN) is the first neural network model for super-resolution reconstruction tasks [35]. The SRCNN consists of a

• three-layer convolutional neural network which directly learns an end-to-end mapping between low- and high-resolution images, making the reconstructed image as close to the original image as possible. Generally, increasing the network depth could improve the reconstruction accuracy. With the development