A Collaborative Despeckling Method for SAR Images Based on Texture Classification

Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise

A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details

Age‐Related Trends in the Predictive Value of Carotid Intima‐Media Thickness for Cardiovascular Death: A Prospective Population‐Based Cohort Study

Background The age‐related trends in the predictive ability of carotid intima‐media thickness (CIMT) for cardiovascular risk remain unclear. We aimed to identify the age‐related trends in the predictive value of CIMT for cardiovascular death. Methods and Results In a prospective cohort of adults aged 35 to 75 years without history of cardiovascular disease who were enrolled between 2014 and 2020, we measured CIMT at baseline and collected the vital status and cause of death. We divided the study population into 4 age groups (35–44, 45–54, 55–64, and 65–75 years). Competing risk models were fitted to estimate the associations between CIMT and cardiovascular death. The added values of CIMT in prediction were assessed by the differences of the Harrell's concordance index and the net reclassification improvement index. We included 369 478 adults and followed them for a median of 4.7 years. A total of 4723 (1.28%) cardiovascular deaths occurred. After adjusting for the traditional risk factors, the hazard ratios for CIMTmean per SD decreased with age, from 1.27 (95% CI, 1.17–1.37) in the 35 to 44 years age group to 1.14 (95% CI, 1.10–1.19) in the 65 to 75 years age group (P for interaction <0.01). Meanwhile, the net reclassification improvement indexes for CIMTmean were attenuated with age, from 22.60% (95% CI, 15.56%–29.64%) in the 35 to 44 years age group to 7.00% (95% CI, −6.82% to 20.83%) in the 65 to 75 years age group. Similar results were found for maximum CIMT in all age groups. Conclusions CIMT may improve cardiovascular risk prediction in the young and middle‐aged populations, rather than those aged ≥55 years

Antimicrobial Activity of Sphingolipids Isolated from the Stems of Cucumber (Cucumis sativus L.)

Three antimicrobial sphingolipids were separated by bioassay-guided isolation from the chloroform fraction of the crude methanol extract of cucumber (Cucumis sativus L.) stems and identified as (2S,3S,4R,10E)-2-[(2\u27R)-2-hydroxytetra-cosanoylamino]-1,3,4-octadecanetriol-10-ene (1), 1-O-β-D-glucopyranosyl(2S,3S,4R,10E)-2-[(2\u27R)-2-hydroxy-tetracosanoylamino]-1,3,4-octadecanetriol-10-ene (2) and soya-cerebroside I (3) by their physicochemical properties and spectroscopic analysis. They were evaluated to show antifungal and antibacterial activity on test microorganisms including four fungal and three bacterial species. Among them, compound 1, a relatively low polarity aglycone, exhibited stronger antimicrobial activity than its corresponding glycoside 2. The results indicated that sphingolipids could be the main antimicrobial compounds in the crude methanol extract of cucumber stems

Root length density at flowering of two rice cultivars under the all irrigation treatments (the CF, DI, FIM, and FIN treatments).

<p>Root length density at flowering (1 August in 2011, and 4 August in 2012) of cultivar Ninggeng28 (japonica) (a, b) and cultivar Xindao17 (japonica) (c) in 2011 (a) and 2012 (b, c). Vertical bars represent ±S.E. of the mean (n=3). Abbreviations are same as Figure 1 and Figure 3.</p