Image Super-Resolution Reconstruction Based On L1/2 Sparsity

Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms

SiamLST: Learning Spatial and Channel-wise Transform for Visual Tracking

Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effectively exploit the spatial and inter-channel cues, which lead to the redundancy of pre-trained model parameters. In this paper, we design a novel visual tracker based on a Learnable Spatial and Channel-wise Transform in Siamese network (SiamLST). The SiamLST tracker includes a powerful feature extraction backbone and an efficient cross-correlation method. The proposed algorithm takes full advantages of CNN and the learnable sparse transform module to represent the template and search patches, which effectively exploit the spatial and channel-wise correlations to deal with complicated scenarios, such as motion blur, in-plane rotation and partial occlusion. Experimental results conducted on multiple tracking benchmarks including OTB2015, VOT2016, GOT-10k and VOT2018 demonstrate that the proposed SiamLST has excellent tracking performances

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

Image Super-Resolution Reconstruction Based on L1/2 Sparsity

Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms

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

Soil microbiome manipulation triggers direct and possible indirect suppression against <i>Ralstonia solanacearum</i> and <i>Fusarium oxysporum</i>

Soil microbiome manipulation can potentially reduce the use of pesticides by improving the ability of soils to resist or recover from pathogen infestation, thus generating natural suppressiveness. We simulated disturbance through soil fumigation and investigated how the subsequent application of bio-organic and organic amendments reshapes the taxonomic and functional potential of the soil microbiome to suppress the pathogens Ralstonia solanacearum and Fusarium oxysporum in tomato monocultures. The use of organic amendment alone generated smaller shifts in bacterial and fungal community composition and no suppressiveness. Fumigation directly decreased F. oxysporum and induced drastic changes in the soil microbiome. This was further converted from a disease conducive to a suppressive soil microbiome due to the application of organic amendment, which affected the way the bacterial and fungal communities were reassembled. These direct and possibly indirect effects resulted in a highly efficient disease control rate, providing a promising strategy for the control of the diseases caused by multiple pathogens

Cadmium suppresses the proliferation of piglet Sertoli cells and causes their DNA damage, cell apoptosis and aberrant ultrastructure

<p>Abstract</p> <p>Objective</p> <p>Very little information is known about the toxic effects of cadmium on somatic cells in mammalian testis. The objective of this study is to explore the toxicity of cadmium on piglet Sertoli cells.</p> <p>Methods</p> <p>Sertoli cells were isolated from piglet testes using a two-step enzyme digestion and followed by differential plating. Piglet Sertoli cells were identified by oil red O staining and Fas ligand (FasL) expression as assayed by immunocytochemistry and expression of transferrin and androgen binding protein by RT-PCR. Sertoli cells were cultured in DMEM/F12 supplemented with 10% fetal calf serum in the absence or presence of various concentrations of cadmium chloride, or treatment with p38 MAPK inhibitor SB202190 and with cadmium chloride exposure. Apoptotic cells in seminiferous tubules of piglets were also performed using TUNEL assay in vivo.</p> <p>Results</p> <p>Cadmium chloride inhibited the proliferation of Piglet Sertoli cells as shown by MTT assay, and it increased malondialdehyde (MDA) but reduced superoxide dismutase (SOD) and Glutathione peroxidase (GSH-Px) activity. Inhibitor SB202190 alleviated the proliferation inhibition of cadmium on piglet Sertoli cells. Comet assay revealed that cadmium chloride caused DNA damage of Piglet Sertoli cells and resulted in cell apoptosis as assayed by flow cytometry. The in vivo study confirmed that cadmium induced cell apoptosis in seminiferous tubules of piglets. Transmission electronic microscopy showed abnormal and apoptotic ultrastructure in Piglet Sertoli cells treated with cadmium chloride compared to the control.</p> <p>Conclusion</p> <p>cadmium has obvious adverse effects on the proliferation of piglet Sertoli cells and causes their DNA damage, cell apoptosis, and aberrant morphology. This study thus offers novel insights into the toxicology of cadmium on male reproduction.</p

A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography

Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and the time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events passively in a single exposure. It possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and plays a revolutionary role in single-shot ultrafast optical imaging. However, due to the ultra-high data compression ratio induced by the extremely large sequence depth as well as the limited fidelities of traditional reconstruction algorithms over the reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we propose a flexible image reconstruction algorithm based on the total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which can preserve the image smoothness while utilizing the deep denoising networks to obtain more priori, and thus solving the common sparsity representation problem in local similarity and motion compensation. Both simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast optical scenes.Comment: 25 pages, 5 figures and 1 tabl

DNA damage and decrease of cellular oxidase activity in piglet sertoli cells exposed to gossypol

The study was designated to explore the toxic effects of gossypol on piglet sertoli cells. Sertoli cells were isolated from piglet testes using a two-step enzyme digestion and followed by differential plating. Piglet sertoli cells were cultured and classified into five groups, that is, group A, the control without gossypol, group B with 2.5 μg/ml gossypol, group C with 5 μg/ml gossypol, group D with 10 μg/ml gossypol and group E with 20 μg/ml gossypol. We found that sertoli cells’ growth was inhibited by gossypol at dose 2.5 μg/ml when compared with the control group. The oxidase activity of sertoli cell also decreased at 2.5 μg/m gossypol. Moreover, DNA damage of sertoli cells was observed at 5 μg/ml gossypol. Putting this into consideration, our study suggests that exposure of gossypol to sertoli cells leads to an inhibition of sertoli cell growth and oxidase activity of sertoli cells at a low concentration, whereas gossypol results in DNA damage of sertoli cells at a higher concentration.Keywords: Gossypol, sertoli cells, oxidase, DNA damag