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Compressed sensing (CS) is a valuable technique for reconstructing measurements in numerous domains. CS has not yet gained widespread adoption in scanning tunneling microscopy (STM), despite potentially offering the advantages of lower acquisition time and enhanced tolerance to noise. Here we applied a simple CS framework, using a weighted iterative thresholding algorithm for CS reconstruction, to representative high-resolution STM images of superconducting surfaces and adsorbed molecules. We calculated reconstruction diagrams for a range of scanning patterns, sampling densities, and noise intensities, evaluating reconstruction quality for the whole image and chosen defects. Overall we find that typical STM images can be satisfactorily reconstructed down to 30% sampling - already a strong improvement. We furthermore outline limitations of this method, such as sampling pattern artifacts, which become particularly pronounced for images with intrinsic long-range disorder, and propose ways to mitigate some of them. Finally we investigate compressibility of STM images as a measure of intrinsic noise in the image and a precursor to CS reconstruction, enabling a priori estimation of the effectiveness of CS reconstruction with minimal computational cost.
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Image recovery from compressive measurements requires a signal prior for the images being reconstructed. Recent work has explored the use of deep generative models with low latent dimension as signal priors for such problems. However, their recovery performance is limited by high representation error. We introduce a method for achieving low representation error using generators as signal priors. Using a pre-trained generator, we remove one or more initial blocks at test time and optimize over the new, higher-dimensional latent space to recover a target image. Experiments demonstrate significantly improved reconstruction quality for a variety of network architectures. This approach also works well for out-of-training-distribution images and is competitive with other state-of-the-art methods. Our experiments show that test-time architectural modifications can greatly improve the recovery quality of generator signal priors for compressed sensing.
We present an improved way for imaging the local density of states with a scanning tunneling microscope, which consists in mapping the surface topography while keeping the differential conductance (d$I$/d$V$) constant. When archetypical C$_{60}$ molecules on Cu(111) are imaged with this method, these so-called iso-d$I$/d$V$ maps are in excellent agreement with theoretical simulations of the isodensity contours of the molecular orbitals. A direct visualization and unambiguous identification of superatomic C$_{60}$ orbitals and their hybridization is then possible.
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details. Nevertheless, the proposed IFR-CS still has some limitations, such as the selection of hyper-parameters, a lengthy reconstruction time, and the fixed sparsifying transform. To alleviate these issues, we unroll the iterative feature refinement procedures in IFR-CS to a supervised model-driven network, dubbed IFR-Net. Equipped with training data pairs, both regularization parameter and the utmost feature refinement operator in IFR-CS become trainable. Additionally, inspired by the powerful representation capability of convolutional neural network (CNN), CNN-based inversion blocks are explored in the sparsity-promoting denoising module to generalize the sparsity-enforcing operator. Extensive experiments on both simulated and in vivo MR datasets have shown that the proposed network possesses a strong capability to capture image details and preserve well the structural information with fast reconstruction speed.
We present a theoretical analysis of the standing wave patterns in STM images, which occur around surface point defects. We consider arbitrary dispersion relations for the surface states and calculate the conductance for a system containing a small-size tunnel contact and a surface impurity. We find rigorous theoretical relations between the interference patterns in the real-space STM images, their Fourier transforms and the Fermi contours of two-dimensional electrons. We propose a new method for reconstructing Fermi contours of surface electron states, directly from the real-space STM images around isolated surface defects.
We investigate the adsorption of a single tetracyanoethylene (TCNE) molecule on the silver (001) surface. Adsorption structures, electronic properties, and scanning tunneling microscopy (STM) images are calculated within density-functional theory. Adsorption occurs most favorably in on-top configuration, with the C=C double bond directly above a silver atom and the four N atoms bound to four neighboring Ag atoms. The lowest unoccupied molecular orbital of TCNE becomes occupied due to electron transfer from the substrate. This state dominates the electronic spectrum and the STM image at moderately negative bias. We discuss and employ a spatial extrapolation technique for the calculation of STM and scanning tunneling spectroscopy (STS) images. Our calculated images are in good agreement with experimental data.
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