Image Structure-Preserving Denoising Based on Difference Curvature Driven Fractional Nonlinear Diffusion

The traditional integer-order partial differential equations and gradient regularization based image denoising techniques often suffer from staircase effect, speckle artifacts, and the loss of image contrast and texture details. To address these issues, in this paper, a difference curvature driven fractional anisotropic diffusion for image noise removal is presented, which uses two new techniques, fractional calculus and difference curvature, to describe the intensity variations in images. The fractional-order derivatives information of an image can deal well with the textures of the image and achieve a good tradeoff between eliminating speckle artifacts and restraining staircase effect. The difference curvature constructed by the second order derivatives along the direction of gradient of an image and perpendicular to the gradient can effectively distinguish between ramps and edges. Fourier transform technique is also proposed to compute the fractional-order derivative. Experimental results demonstrate that the proposed denoising model can avoid speckle artifacts and staircase effect and preserve important features such as curvy edges, straight edges, ramps, corners, and textures. They are obviously superior to those of traditional integral based methods. The experimental results also reveal that our proposed model yields a good visual effect and better values of MSSIM and PSNR

Identification and confirmation of the environmental risks of emerging pollutants in surface waters and sediments

Although the occurrence, the fate and the toxicology of emerging pollutants in the aquatic environment have been widely studied, there is still a lack in the correlation of the levels of pollutants with the possible adverse effects in wildlife. The short comings of traditional methods for risk assessment have been observed, and the contributions of the identified compounds to the observed risks are rarely confirmed. Therefore, the main purpose of this thesis was to develop reasonable methods for risk identification of single compounds and mixtures, and to identify and confirm environmental risks caused by non-specific and mechanism-specific toxicity in aquatic systems. In this thesis, optimized methods for risk identification of single compounds and mixtures were developed. For screening-level risk assessment of single compounds, an optimized risk quotient that considers not only toxicological data but also the frequency with which the detected concentrations exceeded predicted no-effect concentrations was used to screen candidate priority pollutants in European surface waters. Results showed that 45 of the 477 analyzed compounds indicated potential risks for European surface waters. For risk assessment of environmental samples, a risk quotient that considers the ratio of the BEQ to the environmental quality standard (EQS) and the frequency of BEQ exceeding EQS was recommended. Results showed that the highest risk of the anti-androgenic activities was presented at the site directly influenced by the effluents of wastewater treatment plants. To confirm the risk of selected pharmaceuticals, the three antimicrobials clarithromycin, triclosan and sulfamethoxazole, the two anti-inflammatories ibuprofen and diclofenac, the anticonvulsant carbamazepine, the lipid-lowering agent bezafibrate, and the stimulant caffeine were used to study their delayed toxicity using the zebrafish larvae behavioral test. Delayed hatch was observed for exposure to triclosan (1 μg/l) and ibuprofen (100 μg/l) in the early stages of development. In the early stages of development after hatching, the larval locomotor behavior following embryonic exposure to 0.1 μg/l triclosan and 1 μg/l caffeine was altered. Furthermore, for a mixture of the respective highest environmental concentrations of the 8 pharmaceuticals changes in larval behavior were observed. Mechanism-specific bioassays and chemical analyses were performed to identify and confirm endocrine disturbance activities (e.g., anti-androgenic activity) in surface waters and aryl hydrocarbon receptor (AhR)-mediated activities in sediments. Spatial and temporal variations of anti-androgenic activities and environmental risks were observed. High cytotoxicity and anti-androgenic activities were observed in surface waters that were directly influenced by the effluents of wastewater treatment plants. Although sediment samples from the upper Danube River were considered less contaminated, high AhR-mediated activities were observed. Furthermore, by combining bioassays, fractionation, chemical analysis and confirmation to an effect-directed analysis (EDA), fractions with adverse effects were screened and effects further associated to specific pollutants and mixtures. The comparison between measured bioanalytical equivalents (BEQs) of sediment samples and BEQs of synthetic mixtures revealed that the US EPA priority PAHs seem to account only to a minor extent for the AhR-mediated activities of the sediment samples, while non-priority substances in medium-polar and polar fractions were high inducers. In this thesis, induced EROD and endocrine disturbance activities were expressed as BEQs of the respective reference standards. In order to properly use the BEQs and confirm the contributions of chemically an alyzed compounds to bioassay-derived BEQs, the factors causing the variations of BEQs and relative potencies (REPs) were mathematically analyzed. The effects of the effect levels, slopes and the maximum response of the concentration-response curves of the samples and reference compounds on REP and BEQ variations were confirmed. Although bioassay-derived BEQs(Bio-BEQs) and chemically estimated BEQs (Chem-BEQs) vary with the selected effect level, the explanation of Bio-BEQs by Chem-BEQs of certain compounds at the same effect level will be theoretically stable

Audio-Visual-Based Query by Example Video Retrieval

Query by example video retrieval aims at automatic retrieval of video samples which are similar to a user-provided example from video database. Considering that much of prior work on video analysis support retrieval using only visual features, in this paper, a two-step method for query by example is proposed, in which both audio and visual features are used. In the proposed method, a set of audio and visual features are, respectively, extracted from the shot level and key frame level. Among these features, audio features are employed to rough retrieval, while visual features are applied to refine retrieval. The experimental results demonstrate the good performance of the proposed approach

A Fast and Lightweight Detection Network for Multi-Scale SAR Ship Detection under Complex Backgrounds

It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under complex backgrounds. Traditional constant false alarm rate methods are cumbersome in manual design and weak in migration capabilities. Based on deep learning, researchers have introduced methods that have shown good performance in order to get better detection results. However, the majority of these methods have a huge network structure and many parameters which greatly restrict the application and promotion. In this paper, a fast and lightweight detection network, namely FASC-Net, is proposed for multi-scale SAR ship detection under complex backgrounds. The proposed FASC-Net is mainly composed of ASIR-Block, Focus-Block, SPP-Block, and CAPE-Block. Specifically, without losing information, Focus-Block is placed at the forefront of FASC-Net for the first down-sampling of input SAR images at first. Then, ASIR-Block continues to down-sample the feature maps and use a small number of parameters for feature extraction. After that, the receptive field of the feature maps is increased by SPP-Block, and then CAPE-Block is used to perform feature fusion and predict targets of different scales on different feature maps. Based on this, a novel loss function is designed in the present paper in order to train the FASC-Net. The detection performance and generalization ability of FASC-Net have been demonstrated by a series of comparative experiments on the SSDD dataset, SAR-Ship-Dataset, and HRSID dataset, from which it is obvious that FASC-Net has outstanding detection performance on the three datasets and is superior to the existing excellent ship detection methods

A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and noise corruption. To improve the recognition performance, methods based on convolutional neural networks (CNN) have been introduced to solve such problems and have shown outstanding performance. However, most of these methods rely on continuously increasing the width and depth of networks. This adds a large number of parameters and computational overhead, which is not conducive to deployment on edge devices. To solve these problems, a novel lightweight fully convolutional neural network based on Channel-Attention mechanism, Channel-Shuffle mechanism, and Inverted-Residual block, namely the ASIR-Net, is proposed in this paper. Specifically, we deploy Inverted-Residual blocks to extract features in high-dimensional space with fewer parameters and design a Channel-Attention mechanism to distribute different weights to different channels. Then, in order to increase the exchange of information between channels, we introduce the Channel-Shuffle mechanism into the Inverted-Residual block. Finally, to alleviate the matter of the scarcity of SAR images and strengthen the generalization performance of the network, four approaches of data augmentation are proposed. The effect and generalization performance of the proposed ASIR-Net have been proved by a lot of experiments under both SOC and EOCs on the MSTAR dataset. The experimental results indicate that ASIR-Net achieves higher recognition accuracy rates under both SOC and EOCs, which is better than the existing excellent ATR methods

Multi-focus Image Fusion using an Improved Differential Evolution Algorithm and Adaptive Block Mechanism

Multi-focus image fusion based on differential evolution algorithm has been demonstrated to be a simple and efficient method. However, several some problems remain. First, the fusion rulesin which the best block size is selected is only based on the original differential evolution algorithm, which easily falls into the local convergence and thus affects the global search ability. When the sharpness values of the corresponding blocks are equal, the method becomes ineffective, because the block effect is enhanced. Secondthe algorithm ignores image size. Thus, calculating larger pictures becomes complex and time consuming. Clear and fuzzy areas are further divided, thus causing unnecessary calculations. Therefore, a multi-focus images fusion method based on an improved differential evolution algorithm and adaptive block mechanism is presented. First, the source images are divided by a fixed size once. Then, the boundary region is searched to find adaptive blocks using the improved differential evolution algorithm. If the sharpness values of the corresponding blocks are equal, the extends block mechanism is applied to determine the block with the highest sharpness value. Experimental results show that the improved algorithm can obtain better fusion effects and consume less time compared with the original differential evolution algorithm

A Fast and Lightweight Detection Network for Multi-Scale SAR Ship Detection under Complex Backgrounds

It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under complex backgrounds. Traditional constant false alarm rate methods are cumbersome in manual design and weak in migration capabilities. Based on deep learning, researchers have introduced methods that have shown good performance in order to get better detection results. However, the majority of these methods have a huge network structure and many parameters which greatly restrict the application and promotion. In this paper, a fast and lightweight detection network, namely FASC-Net, is proposed for multi-scale SAR ship detection under complex backgrounds. The proposed FASC-Net is mainly composed of ASIR-Block, Focus-Block, SPP-Block, and CAPE-Block. Specifically, without losing information, Focus-Block is placed at the forefront of FASC-Net for the first down-sampling of input SAR images at first. Then, ASIR-Block continues to down-sample the feature maps and use a small number of parameters for feature extraction. After that, the receptive field of the feature maps is increased by SPP-Block, and then CAPE-Block is used to perform feature fusion and predict targets of different scales on different feature maps. Based on this, a novel loss function is designed in the present paper in order to train the FASC-Net. The detection performance and generalization ability of FASC-Net have been demonstrated by a series of comparative experiments on the SSDD dataset, SAR-Ship-Dataset, and HRSID dataset, from which it is obvious that FASC-Net has outstanding detection performance on the three datasets and is superior to the existing excellent ship detection methods

Multimedia Data Fusion

Multimedia data is widely used in the world such as image, video, text, and audio. For obtaining better sensing performance, research on multimedia data fusion (MDF) is active and extensive around the world. In medical systems, a body part could be imaged with different sensors such as computed tomography and magnetic resonance imaging. In video surveillance, the interest is in the identification, recognition, and tracking of people by numerous cameras. All of these cases illustrate the importance of MDF in real-life applications. A number of mathematical methods have been researched in MDF such as statistics, fuzzy mathematical, stochastic differential theory, and computational methods. As a special issue, we aim to reflect on the current and future theory and application of MDF in the following aspects: sensor fusion in multimedia medical data, sensor registration in 3D multimedia data, sensor selection for optimal sensor fusion, performance evaluation of the data fusion algorithms, joint data registration and fusion algorithms and the application of multimedia data fusion