Convolutional Neural Network for Pansharpening with Spatial Structure Enhancement Operator

Pansharpening aims to fuse the abundant spectral information of multispectral (MS) images and the spatial details of panchromatic (PAN) images, yielding a high-spatial-resolution MS (HRMS) image. Traditional methods only focus on the linear model, ignoring the fact that degradation process is a nonlinear inverse problem. Due to convolutional neural networks (CNNs) having an extraordinary effect in overcoming the shortcomings of traditional linear models, they have been adapted for pansharpening in the past few years. However, most existing CNN-based methods cannot take full advantage of the structural information of images. To address this problem, a new pansharpening method combining a spatial structure enhancement operator with a CNN architecture is employed in this study. The proposed method uses the Sobel operator as an edge-detection operator to extract abundant high-frequency information from the input PAN and MS images, hence obtaining the abundant spatial features of the images. Moreover, we utilize the CNN to acquire the spatial feature maps, preserving the information in both the spatial and spectral domains. Simulated experiments and real-data experiments demonstrated that our method had excellent performance in both quantitative and visual evaluation.

Unsupervised Three-Dimensional Tubular Structure Segmentation via Filter Combination

Tubular structure enhancement plays an utmost role in medical image segmentation as a pre-processing technique. In this work, an unsupervised 3D tubular structure segmentation technique is developed, which is mainly inspired by the idea of filter combination. Three well-known vessel filters, Frangi’s filter, the modified Frangi’s filter and the Multiscale Fractional Anisotropic Tensor (MFAT) filter, separately enhance the original images. Next, the enhanced images obtained using three different filters are combined. Different categories of vessel filters have the ability of complementarity, which is the main motivation of combining these three advanced filters. The combination of them ensures a high diversity of the enhancing results. Weighted mean and median ranking methods are used to conduct the operation of filter combination. Based on the optimized weights for all the three individual filters, fuzzy C-means method is then applied to segment the tubular structures. The proposed technique is tested on the public DRIVE and STARE datasets, the public synthetic vascular models (2011 and 2013 VascuSynth Sample), and real-patient Coronary Computed Tomography Angiography (CCTA) datasets. Experimental results demonstrate that the proposed technique outperforms the state-of-the-art filter combination-based segmentation methods. Moreover, our proposed method is able to yield better tubular structure segmentation results than that of each individual filter, which exhibits the superiority of the proposed method. In conclusion, the proposed method can be further used to facilitate vessel segmentation in medical practice.

Data-driven edge detectors for seismic data interpretation

We present a novel method to assist in seismic interpretation. The algorithm learns data-driven edge-detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local and structural information retrieved from patches taken randomly from the input time slices. The filters conform to an orthogonal family that behaves as structure-aware Sobel-like edge detectors, and the user can set their size and number. The results from marine Canada and New Zealand 3D seismic data demonstrate that the proposed algorithm allows the semblance attribute to improve the delineation of the subsurface channels. This fact is further supported by testing the method with realistic synthetic 2D and 3D data sets containing channeling and meandering systems. We contrast the results with standard plain Sobel filtering, multidirectional Sobel filters of variable size, and the dip-oriented plane-wave destruction Sobel attribute. The proposed method gives results that are comparable or superior to those of Sobel-based approaches. In addition, the obtained filters can adapt to the geological structures present in each time slice, which reduces the number of unwanted artifacts in the final product.

Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2

Reconstructions of structure of biomolecules, for instance via X-ray crystallography or cryo-EM frequently contain clashes of atomic centers. Correction methods are usually based on simulations approximating biophysical chemistry, making them computationally expensive and often not correcting all clashes. We propose a computationally fast data-driven statistical method yielding suites free from within-suite clashes: From such a clash free training data set, devising mode hunting after torus PCA on adaptive cutting average linkage tree clustering (MINTAGE), we learn RNA suite shapes. With classification based on multiscale structure enhancement (CLEAN), for a given clash suite we determine its neighborhood on a mesoscopic scale involving several suites. As corrected suite we propose the Fréchet mean on a torus of the largest classes in this neighborhood. We validate CLEAN MINTAGE on a benchmark data set, compare it to a state of the art correction method and apply it, as proof of concept, to two exemplary suites adjacent to helical pieces of the frameshift stimulation element of SARS-CoV-2 which are difficult to reconstruct. In contrast to a recent reconstruction proposing several different structure models, CLEAN MINTAGE unanimously proposes structure corrections within the same clash free class for all suites.

A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture

Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between images and the lack overall structure enhancement, and do not fully and completely research optimization goals and fusion rules. Therefore, for these problems, we propose a pansharpening generative adversarial network with multilevel structure enhancement and a multistream fusion architecture. This method first uses multilevel gradient operators to obtain the structural information of the high-resolution panchromatic image. Then, it combines the spectral features with multilevel gradient information and inputs them into two subnetworks of the generator for fusion training. We design a comprehensive optimization goal for the generator, which not only minimizes the gap between the fused image and the real image but also considers the adversarial loss between the generator and the discriminator and the multilevel structure loss between the fused image and the panchromatic image. It is worth mentioning that we comprehensively consider the spectral information and the multilevel structure as the input of the discriminator, which makes it easier for the discriminator to distinguish real and fake images. Experiments show that our proposed method is superior to state-of-the-art methods in both the subjective visual and objective assessments of fused images, especially in road and building areas.

Book Review: Daniel P. Mears, Out-of-Control Criminal Justice: The Systems Improvement Solution for More Safety, Justice, Accountability, and Efficiency. New York: Cambridge University Press. 2017.

In his book, Out-of-Control Criminal Justice, the Author, Daniel P. Mears provides readers a comprehensive look at the criminal justice system and the need for a system-based approach to criminal justice reform. The book offers a summary of the latest issues in the criminal justice system and the advancement of criminal justice reform. A description of mechanisms and the existence of device problems is given by the author. He describes protection, fairness, transparency, and efficacy as the four primary priorities of the criminal justice system. The book outlines how the new strategy of criminal justice struggles to adequately meet such aims. Mears discusses how an approach to structure enhancement solutions will better meet the aims of the criminal justice system.

Targeting of CAT and VCAM1 as Novel Therapeutic Targets for DMD Cardiomyopathy

Duchenne muscular dystrophy (DMD) related cardiomyopathy is the leading cause of early mortality in DMD patients. There is an urgent need to gain a better understanding of the disease molecular pathogenesis and develop effective therapies to prevent the onset of heart failure. In the present study, we used DMD human induced pluripotent stem cells (DMD-hiPSCs) derived cardiomyocytes (CMs) as a platform to explore the active compounds in commonly used Chinese herbal medicine (CHM) herbs. Single CHM herb (DaH, ZK, and CQZ) reduced cell beating rate, decreased cellular ROS accumulation, and improved structure of DMD hiPSC-CMs. Cross-comparison of transcriptomic profiling data and active compound library identified nine active chemicals targeting ROS neutralizing Catalase (CAT) and structural protein vascular cell adhesion molecule 1 (VCAM1). Treatment with Quecetin, Kaempferol, and Vitamin C, targeting CAT, conferred ROS protection and improved contraction; treatment with Hesperidin and Allicin, targeting VCAM1, induced structure enhancement via induction of focal adhesion. Lastly, overexpression of CAT or VCAM1 in DMD hiPSC-CMs reconstituted efficacious effects and conferred increase in cardiomyocyte function. Together, our results provide a new insight in treating DMD cardiomyopathy via targeting of CAT and VCAM1, and serves as an example of translating Bed to Bench back to Bed using a muti-omics approach.

A methylation-inspired mesoporous coordination polymer for identification and removal of organic pollutants in aqueous solutions

Tuning the interfacial chemistry of a metal–biomolecule coordination hybrid via bio-inspired methylation for structure enhancement and selective hazard adsorption/identification.