Tensor Completion Using Bilayer Multimode Low-Rank Prior and Total Variation.

In this article, we propose a novel bilayer low-rankness measure and two models based on it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first encoded by LR matrix factorizations (MFs) to the all-mode matricizations, which can exploit multiorientational spectral low rankness. Presumably, the factor matrices of all-mode decomposition are LR, since local low-rankness property exists in within-mode correlation. In the decomposed subspace, to describe the refined local LR structures of factor/subspace, a new low-rankness insight of subspace: a double nuclear norm scheme is designed to explore the so-called second-layer low rankness. By simultaneously representing the bilayer low rankness of the all modes of the underlying tensor, the proposed methods aim to model multiorientational correlations for arbitrary N -way ( N ≥ 3 ) tensors. A block successive upper-bound minimization (BSUM) algorithm is designed to solve the optimization problem. Subsequence convergence of our algorithms can be established, and the iterates generated by our algorithms converge to the coordinatewise minimizers in some mild conditions. Experiments on several types of public datasets show that our algorithm can recover a variety of LR tensors from significantly fewer samples than its counterparts.

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Image Degradation in Microscopic Images: Avoidance, Artifacts, and Solutions.

The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. The first step is to precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition. A better object representation allows biological users to draw more reliable scientific conclusions. Image restoration can manipulate the acquired data in an effort to reduce the impact of artifacts (spurious results) due to physical and technical limitations, resulting in a better representation of the object of interest. However, precise usage of these algorithms is necessary so as to avoid further artifacts that might influence the data analysis and bias the conclusions. It is essential to understand image acquisition, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. This paper provides an overview of the fundamental artifacts and degradations that affect many micrographs. We describe why artifacts appear, in what sense they impact overall image quality, and how to mitigate them by first improving the acquisition parameters and then applying proper image restoration techniques.