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Institution

Nanjing University of Information Science and Technology

EducationNanjing, China
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.


Papers
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Journal ArticleDOI
TL;DR: A new taxonomy based on image representations is introduced for a better understanding of state-of-the-art image denoising techniques and methods based on overcomplete representations using learned dictionaries perform better than others.
Abstract: Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.

376 citations

Journal ArticleDOI
TL;DR: It is demonstrated that water management Si fertilization, and selection of rice cultivars are effective measures that can be used to reduce As accumulation in rice.
Abstract: Rice represents a major route of As exposure in populations that depend on a rice diet. Practical measures are needed to mitigate the problem of excessive As accumulation in paddy rice. Two potential mitigation methods, management of the water regime and Si fertilization, were investigated under greenhouse conditions. Growing rice aerobically during the entire rice growth duration resulted in the least As accumulation. Maintaining aerobic conditions during either vegetative or reproductive stage of rice growth also decreased As accumulation in rice straw and grain significantly compared with rice grown under flooded conditions. The effect of water management regimes was consistent with the observed effect of flooding-induced arsenite mobilization in the soil solution. Aerobic treatments increased the percentage of inorganic As in grain, but the concentrations of inorganic As remained lower than in the flooded rice. Silicon fertilization decreased the total As concentration in straw and grain by 78 and 16%, respectively, even though Si addition increased As concentration in the soil solution. Silicon also significantly influenced As speciation in rice grain and husk by enhancing methylation. Silicon decreased the inorganic As concentration in grain by 59% while increasing the concentration of dimethylarsinic acid (DMA) by 33%. There were also significant differences between two rice genotypes in grain As speciation. This study demonstrated that water management, Si fertilization, and selection of rice cultivars are effective measures that can be used to reduce As accumulation in rice.

361 citations

Journal ArticleDOI
TL;DR: This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels.
Abstract: This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels Using the characteristic function of difference histogram (DHCF), we prove that the center of mass of DHCF (DHCF COM) decreases after messages are embedded Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well

359 citations

Journal ArticleDOI
TL;DR: It is presented that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking.
Abstract: Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.

353 citations

Journal ArticleDOI
TL;DR: Among different Pb-remediation approaches, certain advanced approaches such as microbial assisted phytoremediation which could possibly minimize the Pb load from the resources in a sustainable manner and would be a viable option to ensure a safe food production system are highlighted.
Abstract: Lead (Pb) toxicity has been a subject of interest for environmental scientists due to its toxic effect on plants, animals, and humans. An increase in several Pb related industrial activities and use of Pb containing products such as agrochemicals, oil and paint, mining, etc. can lead to Pb contamination in the environment and thereby, can enter the food chain. Being one of the most toxic heavy metals, Pb ingestion via the food chain has proven to be a potential health hazard for plants and humans. The current review aims to summarize the research updates on Pb toxicity and its effects on plants, soil, and human health. Relevant literature from the past 20 years encompassing comprehensive details on Pb toxicity has been considered with key issues such as i) Pb bioavailability in soil, ii) Pb biomagnification, and iii) Pb- remediation, which has been addressed in detail through physical, chemical, and biological lenses. In the review, among different Pb-remediation approaches, we have highlighted certain advanced approaches such as microbial assisted phytoremediation which could possibly minimize the Pb load from the resources in a sustainable manner and would be a viable option to ensure a safe food production system.

351 citations


Authors

Showing all 14448 results

NameH-indexPapersCitations
Ashok Kumar1515654164086
Lei Zhang135224099365
Bin Wang126222674364
Shuicheng Yan12381066192
Zeshui Xu11375248543
Xiaoming Li113193272445
Qiang Yang112111771540
Yan Zhang107241057758
Fei Wang107182453587
Yongfa Zhu10535533765
James C. McWilliams10453547577
Zhi-Hua Zhou10262652850
Tao Li102248360947
Lei Liu98204151163
Jian Feng Ma9730532310
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023173
2022552
20213,000
20202,492
20192,221
20181,822