A Comparison of Image Denoising Methods

The advancement of imaging devices and countless images generated everyday pose an increasingly high demand on image denoising, which still remains a challenging task in terms of both effectiveness and efficiency. To improve denoising quality, numerous denoising techniques and approaches have been proposed in the past decades, including different transforms, regularization terms, algebraic representations and especially advanced deep neural network (DNN) architectures. Despite their sophistication, many methods may fail to achieve desirable results for simultaneous noise removal and fine detail preservation. In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We also introduce a new dataset for benchmarking, and the evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Our experiments demonstrate: (i) the effectiveness and efficiency of representative traditional denoisers for various denoising tasks, (ii) a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts, and (iii) the notable achievements of DNN models, which exhibit impressive generalization ability and show state-of-the-art performance on various datasets. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets, code and results are made publicly available and will be continuously updated at https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising methods to investigate their effectiveness, efficiency, applicability and generalization ability with both synthetic and real-world experiment

Approximate Sparse Regularized Hyperspectral Unmixing

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than l1 regularizer and much easier to be solved than l0 regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to l1 regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art l1 regularizer

Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction

Recent developments in compressive sensing (CS) show that it is possible to accurately reconstruct the magnetic resonance (MR) image from undersampled k-space data by solving nonsmooth convex optimization problems, which therefore significantly reduce the scanning time. In this paper, we propose a new MR image reconstruction method based on a compound regularization model associated with the nonlocal total variation (NLTV) and the wavelet approximate sparsity. Nonlocal total variation can restore periodic textures and local geometric information better than total variation. The wavelet approximate sparsity achieves more accurate sparse reconstruction than fixed wavelet l0 and l1 norm. Furthermore, a variable splitting and augmented Lagrangian algorithm is presented to solve the proposed minimization problem. Experimental results on MR image reconstruction demonstrate that the proposed method outperforms many existing MR image reconstruction methods both in quantitative and in visual quality assessment

Use of the 2A Peptide for Generation of Multi-Transgenic Pigs through a Single Round of Nuclear Transfer

Multiple genetic modifications in pigs can essentially benefit research on agriculture, human disease and xenotransplantation. Most multi-transgenic pigs have been produced by complex and time-consuming breeding programs using multiple single-transgenic pigs. This study explored the feasibility of producing multi-transgenic pigs using the viral 2A peptide in the light of previous research indicating that it can be utilized for multi-gene transfer in gene therapy and somatic cell reprogramming. A 2A peptide-based double-promoter expression vector that mediated the expression of four fluorescent proteins was constructed and transfected into primary porcine fetal fibroblasts. Cell colonies (54.3%) formed under G418 selection co-expressed the four fluorescent proteins at uniformly high levels. The reconstructed embryos, which were obtained by somatic cell nuclear transfer and confirmed to express the four fluorescent proteins evenly, were transplanted into seven recipient gilts. Eleven piglets were delivered by two gilts, and seven of them co-expressed the four fluorescent proteins at equivalently high levels in various tissues. The fluorescence intensities were directly observed at the nose, hoof and tongue using goggles. The results suggest that the strategy of combining the 2A peptide and double promoters efficiently mediates the co-expression of the four fluorescent proteins in pigs and is hence a promising methodology to generate multi-transgenic pigs by a single nuclear transfer

Improved Imputation of Common and Uncommon Single Nucleotide Polymorphisms (SNPs) with a New Reference Set

Statistical imputation of genotype data is an important technique for analysis of genome-wide association studies (GWAS). We have built a reference dataset to improve imputation accuracy for studies of individuals of primarily European descent using genotype data from the Hap1, Omni1, and Omni2.5 human SNP arrays (Illumina). Our dataset contains 2.5-3.1 million variants for 930 European, 157 Asian, and 162 African/African-American individuals. Imputation accuracy of European data from Hap660 or OmniExpress array content, measured by the proportion of variants imputed with R^2^>0.8, improved by 34%, 23% and 12% for variants with MAF of 3%, 5% and 10%, respectively, compared to imputation using publicly available data from 1,000 Genomes and International HapMap projects. The improved accuracy with the use of the new dataset could increase the power for GWAS by as much as 8% relative to genotyping all variants. This reference dataset is available to the scientific community through the NCBI dbGaP portal. Future versions will include additional genotype data as well as non-European populations

Detectable clonal mosaicism and its relationship to aging and cancer

In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases

How to Decouple Tourism Growth from Carbon Emissions? A Spatial Correlation Network Analysis in China

This research aims to analyze the spatial correlation network of the decoupling between tourism growth and carbon emissions in China’s 31 provinces to promote the overall decoupling through regional cooperation. This study scientifically measures the decoupling index from 2009 to 2019 based on a “bottom-up” method and the Tapio decoupling model. It analyzes the spatial correlation network of the decoupling and its driving factors by using social network analysis. The conclusions show that the decoupling between China’s tourism economic growth and carbon emissions was dominated by an expansive connection, which indicates a nonideal decoupling state. Among the regions, decoupling was stronger in the eastern provinces and weaker in the middle and western districts. The spatial correlation outside the plates was more significant, while the internal correlation was weaker. Beijing and Shanghai were in the center of the network, and the eastern developed provinces were in the subcentral place, both of which had more muscular control over the network. In contrast, the middle and western regions were on edge positions, playing passive roles in the network. In addition, the economic development level was the most vital driving force behind the spatial correlation, followed by spatial adjacency and government policy. In contrast, the industrial structure and technological level were negative influencing factors. These research findings indicate potential interprovincial cooperation in terms of tourism decarbonization, which provide a profound reference for the whole sustainable development of China’s tourism industry

The moderating role of informatization between country risks and international tourism: A cross-country panel analysis.

Informatization plays an increasingly important role in the tourism industry, while its effectiveness in alleviating tourism risks remains to be verified. This research aims to explore the effects of country risks on the international tourism and the moderating role of informatization between the two. This study firstly measures country risks based on the ICRG database, quantifies international tourism by tourism revenue, tourism expenditure, and tourist arrival, and calculates informatization level from informatization facilities, informatization applications, and informatization skills. A dynamic SYS-GMM model is then adopted to verify the research hypotheses based on the panel data of 138 countries from 2000 to 2019. The research results show that the composite country risk, political risk, economic risk, and financial risk all show a negative impact on the international tourism indicators regardless of different time periods, regions, or income levels. However, the effects are more obvious before the global financial crisis in 2008 and regions and countries with lower income levels. In addition, informatization is found to positively mitigate the adverse impacts of country risks on international tourism, especially for economic and financial risks. The research findings indicate the risk hedge potential of informatization in the tourism industry, which provides a profound reference for destination risk management

High-Order Total Bounded Variation Model and Its Fast Algorithm for Poissonian Image Restoration

In this paper, a new model for image restoration under Poisson noise based on a high-order total bounded variation is proposed. Existence and uniqueness of its solution are proved. To find the global optimal solution of our strongly convex model, a split Bregman algorithm is introduced. Furthermore, a rigorous convergence theory of the proposed algorithm is established. Experimental results are provided to demonstrate the effectiveness and efficiency of the proposed method over the classic total bounded variation-based model