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Single image deraining (SIDR) often suffers from over/under deraining due to the nonuniformity of rain densities and the variety of raindrop scales. In this paper, we propose a continuous density-guided network (CODE-Net) for SIDR. Particularly, it is composed of a rain streak extractor and a denoiser, where the convolutional sparse coding (CSC) is exploited to filter out noises from the extracted rain streaks. Inspired by the reweighted iterative soft-threshold (ISTA) for CSC, we address the problem of continuous rain density estimation by learning the weights with channel attention blocks from sparse codes. We further develop a multiscale strategy to depict rain streaks appearing at different scales. Experiments on synthetic and real-world data demonstrate the superiority of our methods over recent state-of-the-arts, in terms of both quantitative and qualitative results. Additionally, instead of quantizing rain density with several levels, our CODE-Net can provide continuous-valued estimations of rain densities, which is more desirable in real applications.

Rain streaks are one of the main factors that degrade the performance of computer vision algorithms. Therefore, a preprocessing method is needed to remove rain streaks from rainy images. The main issue of the rain removal task is to prevent over (or under) de-raining. Over de-raining means that the background details are removed along with rain streaks in light rain, and under de-raining means that the rain streaks are not completely removed in heavy rain. These occur as the density of rain and intensity of rain streaks vary. In order to solve this, this paper proposes a two-step rain removal method. The proposed system first estimates the rain streaks image redefined with a simple operation from an input rainy image. The proposed rain streaks image contains rain density and rain streak intensity for the rainy image. By using this, the proposed system can adaptively remove rain streaks from images captured in various rain conditions. In addition, we propose a novel architectural unit, the elementwise attentive gating block, which is an optimized block used to deal with high frequency rain streaks. The proposed block selectively passes the desired components from the input feature maps by applying different weights to each element. It helps to clearly extract the rain streaks, and as a result, there are no traces of rain streaks on the restored image. The proposed method outperforms previous rain removal algorithms for both synthetic and real-world images.

Raining is a common weather condition, and may seriously degrade the performances of outdoor computer vision systems, such as surveillance and autonomous navigation. Rain streaks may exhibit diverse appearances in the captured images, depending on their distances from the camera. For example, sparse rain streaks near the camera lens may appear as continuous and translucent strips, while distant densely accumulated rain streaks are more like fog and mist. Existing rain removal methods are mainly based on a single input image. However, on a single image, it is difficult to estimate a reliable depth map for rain removal. A light field image (LFI) records abundant structural and texture information of the target scene by capturing multi-perspective sub-aperture views with a single exposure. With a LFI, it is easier to estimate the depth maps, and rain streak locations across sub-aperture views are highly correlated. We observe that rain streaks usually have different slops and/or chromaic values, compared with the background scene, along the epipolar plane images (EPIs) of an LFI. Thus, we propose to make use of 3D EPIs to detect rain streaks and restore the background. To this end, we propose a novel GAN architecture to remove rain streaks from an LFI. Our method takes as input a 3D EPI, i.e., a stacked of sub-aperture views along the same row of a rainy LFI. It first estimates the disparity maps for the 3D EPI by utilizing an auto-encoder based depth estimation sub-network. The disparity maps concatenated with the input sub-aperture views are then fed into a non-local residual block, and two branched autoencoder sub-networks are used to extract rain-streaks and recover rain-free sub-aperture views. Extensive experiments conducted on both synthetic real-world-like LFIs and real-world LFIs demonstrate the effectiveness of our method.

The performance of turbomachinery is largely affected by the nonuniform boundary conditions caused by the coupling between neighboring parts, such as the inlet distortion and hot streak. Existing works study this problem by comparing the flow fields with uniform and nonuniform boundary conditions, which is cost extensive. In this work a new and efficient method is developed by computing the sensitivities of arbitrary performance metric relative to the boundary conditions and this method quantitatively describes how the disturbance on the boundary surface affects the performance. The adjoint method popular in the design optimization is extended to current field for the efficient computation of the sensitivity. The extra cost is independent of the number of parameters describing the nonuniform distribution. Accuracy of the adjoint boundary sensitivity is validated against analytical results for a converging–diverging nozzle case. Its applications in a representative case are detailed and discussed in this paper. Current method provides a new tool, also direct guides to minimize the negative effect and improve the performance utilizing the adjoint boundary sensitivity.

Small-angle X-ray scattering (SAXS) coupled with computed tomography (CT), denoted SAXS-CT, enables the spatial distribution of the characteristic parameters of nanoscale structures inside samples to be visualized. In this work, a new scheme with Tikhonov regularization was developed to remove the effects of artifacts caused by streak scattering originating from contour regions of the sample., Small-angle X-ray scattering (SAXS) coupled with computed tomography (CT), denoted SAXS-CT, has enabled the spatial distribution of the characteristic parameters (e.g. size, shape, surface, length) of nanoscale structures inside samples to be visualized. In this work, a new scheme with Tikhonov regularization was developed to remove the effects of artifacts caused by streak scattering originating from the reflection of the incident beam in the contour regions of the sample. The noise due to streak scattering was successfully removed from the sinogram image and hence the CT image could be reconstructed free from artifacts in the contour regions.

Streak tubes with large-format and high spatial resolution are central to mm-spatial-resolved STIL detection system and hyperspectral resolved ICF experiment. In this paper, we established a large-format streak tube with a three-coaxial-cylindrical single-lens focusing system, a spherically curved photocathode and phosphor screen model in CST Particle Studio. The temporal and spatial resolution were calculated and mimicked based on the Monte-Carlo sampling method in static and dynamic mode. The simulated results show that the static spatial resolution reaches 50 lp/mm over the whole 50 mm effective photocathode length, and the physical temporal resolution is better than 45 ps. Furthermore, in dynamic working mode, the streak tube can achieve spatial resolution of 10 lp/mm and temporal resolution of 60 ps. The simulation results will be used to guide the design and production for large-format with high spatial resolution streak tube development.

The performance of vision systems can be affected when used in severe weather conditions such as heavy rain or snow. Rain streak removal is an ill posed problem as they can vary in shape, size, and density across the image. In this paper, a single-image deraining network named Factorized Multi-scale Multi-resolution Residual Network (FMMRNet), which follows a U-Net backbone, is proposed. As rain streaks affect non-local regions of the image, larger receptive fields are beneficial to capture these non-local dependencies. We propose the use of multi-scale grouped convolutions to integrate information from global and local scales. In the proposed FMMRNet, multi-scale convolutions are factorized so that they can have large effective kernel sizes while reducing the computational complexity. During training, intermediate multi-resolution outputs are produced for loss computation which improves gradient flow in the deeper layers of the network and promotes better learning. A channel-wise attention mechanism is included to recalibrate feature maps before fusion instead of direct concatenation at each stage of the decoder. A higher-level reconstruction loss called perceptual loss is included for effective training to improve the visual quality of the derained images. The performance of the proposed FMMRNet is quantitatively and qualitatively compared on public benchmark datasets, and it outperforms the state-of-the-art deraining methods. Furthermore, to show the practical applicability of the proposed network, we demonstrate that when FMMRNet is used as a preprocessing step for object-detection methods such as Faster-RCNN and YOLO, it improves their performance on images degraded by rain streaks by 12.6% and 75.2% respectively.

We propose a method based on a global anisotropic gradient prior (GAGP) for addressing the problem of rain streak removal. We observe that both images and rain streaks have anisotropy at edges, and that the rain distribution in images is sparse. We therefore propose an L0-regularized sparse term with an L0-regularized GAGP term to efficiently detect the rain streaks. We applied the L0-regularized GAGP as the image regularization term to protect the image structure and details. We also developed an alternating half-quadratic algorithm to solve the proposed L0 optimization model by introducing the variable splitting method and analyzing the convergence of the algorithm. Experimental results show that the proposed method outperforms state-of-the-art methods in rain streak removal and preserving image structure on both synthesized and real-world images with different levels and types of rain.

Recently, deep convolutional neural networks have achieved great success for single-image deraining. However, affected by the intrinsic overlapping between rain streaks and background texture patterns, a majority of these methods tend to almost remove texture details in rain-free regions and lead to over-smoothing effects in the recovered background. To generate reasonable rain streak layers and improve the reconstruction quality of the background, we propose a distributed feedback network (DFN) in recurrent structure . A novel feedback block is designed to implement the feedback mechanism. In each feedback block, the hidden state with high-level information (output) will flow into the next iteration to correct the low-level representations (input). By stacking multiple feedback blocks, the proposed network where the hidden states are distributed can extract powerful high-level representations for rain streak layers. Curriculum learning is employed to connect the loss of each iteration and ensure that hidden states contain the notion of output. In addition, a self-ensemble strategy for rain removal task, which can retain the approximate vertical character of rain streaks, is explored to maximize the potential performance of the deraining model. Extensive experimental results demonstrated the superiority of the proposed method in comparison with other deraining methods.

The economic importance of the maize streak virus disease to the African maize production dynamic is to be appreciated now more than ever due to the preponderant influence of a changing climate. Continued dependence on a single major-effect quantitative trait locus (QTL) called Msv1 on Chromosome 1 of Maize (Zea mays L.) is not guaranteed to ensure durable resistance to the causal pathogen. With over ten decades of research on the disease and its associated host plant resistance mechanisms, it is pertinent to consider future approaches to attaining durability by looking to the synergistic roles of moderate- and minor-effect QTLs located on other chromosomes so as to facilitate a secure farming system for sub-Saharan Africa. For this review, more than 40 publications relating to maize streak disease research were methodically analysed with about 30% making specific reference to conventional, molecular and transgenic approaches employed in introgressing, maintaining and improving streak resistance in maize. A meta-analysis of mapped QTLs conferring streak resistance was conducted in a bid to reveal any inter-dependence or co-localization of resistant loci and to aid decision-making for marker-assisted breeding. With the changing climatic conditions around the globe, man's preparedness in the event of an epidemic following any evolutionary process in the streak viral genome was determined as insufficient. Modern breeding approaches including gene pyramiding that could be considered in maize breeding programmes to ensure durability for streak resistance were proposed while improving maize for other abiotic stress tolerance, particularly drought.

Introduction Deep learning (DL) is used to classify, detect, and quantify gold nanoparticles (AuNPs) in a human-sized phantom with a clinical MDCT scanner. Methods AuNPs were imaged at concentrations between 0.0274 and 200 mgAu/mL in a 33 cm phantom. 1 mm-thick CT image slices were acquired at 120 kVp with a CTDIvol of 23.6 mGy. A convolutional neural network (CNN) was trained on 544 images to classify 17 different tissue types and AuNP concentrations. A second set of 544 images was then used for testing. Results AuNPs were classified with 95% accuracy at 0.1095 mgAu/mL and 97% accuracy at 0.2189 mgAu/mL. Both these concentrations are lower than what humans can visually perceive (0.3–1.4 mgAu/mL). AuNP concentrations were also classified with 95% accuracy at 150 and 200 mgAu/mL. These high concentrations result in CT numbers that are at or above the 12-bit limit for CT’s dynamic range where extended Hounsfield scales are otherwise required for measuring differences in contrast. Conclusions We have shown that DL can be used to detect AuNPs at concentrations lower than what humans can visually perceive and can also quantify very high AuNP concentrations that exceed the typical 12-bit dynamic range of clinical MDCT scanners. This second finding is possible due to inhomogeneous AuNP distributions and characteristic streak artifacts. It may even be possible to extend this approach beyond AuNP imaging in CT for quantifying high density objects without extended Hounsfield scales.

This work presents a gated non-local deep residual learning framework for image deraining. It can avoid the over-deraining or under-deraining caused by the global residual learning in existing deraining networks, since the learned soft gate in our method adaptively adjusts the amount of global residual to be passed for generating the final derained result. To generate feature maps for global residual prediction, we develop a non-local guided attention module (NLAM), which first obtains non-local features by exploiting spatial inter-dependencies among all the feature positions of local features produced by convolutional neural network (CNN), and then leverages the attention mechanism to merge the local and non-local features based on their complementary relation. Moreover, we develop a channel-wise gated prediction module to learn a soft gate on the global residual by explicitly modelling channel inter-dependencies of the feature maps obtained from NLAM. Experiments on four deraining benchmark datasets and real-world rainy images show that our network has a quantitative and qualitative improvement over state-of-the-arts.

In this paper, we present an algorithm developed to detect Earth-orbiting object streaks within astronomical images. The NASA Wide-field Infrared Survey Explorer (WISE) database of imagery was selected, in order to assess the viability of infrared wavelengths for the detection and analysis of satellite, rocket body and debris objects. The algorithm was designed to scan a high volume of images for the presence of streaks and then to correlate these to known Earth-orbiting objects without the use of a-priori knowledge of position or orbit parameters of the detected object. As part of the streak analysis process, the algorithm performed a photometric characterisation of the detected objects in all four infrared bands observed by the WISE satellite. The performance of the photometric analysis module was validated against known stellar magnitudes as well as the results of previous published research.

Here, streak noise is modeled in the sinogram domain as correlated noise, which is attenuated by the Block-Matching and 3-D (BM3D) filtering algorithm applied across multiple scales. The proposed procedure is fully automatic and attenuates streak noise and the corresponding ring artifacts without creating major distortions common to other streak removal algorithms., X-ray micro-tomography systems often suffer severe ring artifacts in reconstructed images. These artifacts are caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. In this work, these streaks are modeled in the sinogram domain as additive stationary correlated noise upon logarithmic transformation. Based on this model, a streak removal procedure is proposed where the Block-Matching and 3-D (BM3D) filtering algorithm is applied across multiple scales, achieving state-of-the-art performance in both real and simulated data. Specifically, the proposed fully automatic procedure allows for attenuation of streak noise and the corresponding ring artifacts without creating major distortions common to other streak removal algorithms.

Artifacts arising when differential phase images are integrated is a common problem to several X-ray phase-based experimental techniques. The combination of noise and insufficient sampling of the high-frequency differential phase signal leads to the formation of streak artifacts in the projections, translating into poor image quality in the tomography slices. In this work, we apply a non-iterative integration algorithm proven to reduce streak artifacts in planar (2D) images to a differential phase tomography scan. We report on how the reduction of streak artifacts in the projections improves the quality of the tomography slices, especially in the directions different from the reconstruction plane. Importantly, the method is compatible with large tomography datasets in terms of computation time.

Cash flows to hedge funds are highly sensitive to performance streaks, a streak being defined as subsequent quarters during which a fund performs above or below a benchmark, even after controlling for a wide range of common performance measures. At the same time, streaks have limited predictive power regarding future fund performance. This suggests investors weigh information suboptimally, and their decisions are driven too strongly by a belief in continuation of good performance, consistent with the “hot hand fallacy.” The hedge funds that investors choose to invest in do not perform significantly better than those they divest from. These findings are consistent with overreaction to certain types of information and do not support the notion that sophisticated investors have superior information or superior information processing abilities. This paper was accepted by David Simchi-Levi, finance.

The quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

There is a paucity of studies on the relationship between the control parameters and kinetic parameters in binary-size granular segregation models. In this study, the machine vision method and particle image velocimetry (PIV) were used to measure the shape index of segregation pattern and streamwise velocity, respectively. Two scaled parameters, control parameters and kinetic parameters, were calculated based on the continuous flow model and segregation model. The control parameters include volume flow rate, and the kinetic parameters include the ratio of segregation effects to diffusion effects. By analyzing the relationship between the two parameters under the core and streak patterns, the configuration of segregation pattern at steady-state changes with the control parameters wherein the ratio of segregation effects to diffusion effects decreases approximately linearly as the volume flow rate decreases. These experimental results aid in revealing the mechanism of segregation and providing the reference data for improving the theoretical models.

A point source of X-ray radiation with an energy range of up to 40 keV has been developed on the basis of a specially designed image converter tube. The source is capable of operating in pulsed and continuous modes. The main purpose of this is to use the source for testing X-ray streak cameras. The device can also be used in X-ray microscopy and spectroscopy as a reference radiation source, in biomedical research, and in other fields.

Techniques are described that make it possible to measure the X-ray transport velocity in closed cavities, the time of radiation heating of foils, the radiation temperature, and the timing parameters of X-ray pulses in experiments at the ISKRA-5 facility. The methods are based on position-sensitive time-resolving (with a spatial resolution of 150 µm and a time resolution of 50 ps) measurements of X-rays in four narrow spectral regions of 0.2–1.0 keV using X-ray streak cameras, as well as on multiframe recording (with a frame duration of 100 ps, ​​a frame number of 10, and a spatial resolution of 30 µm). In the experiments the peak Planck temperature of the radiation was 110–150 eV in the irradiating target and 50–90 eV in the additional box and behind the foils; the X-ray transport velocity in closed cavities ranged from 0.5 to 13 mm/ns and the time of thermal breakdown of foils was in range of 50–550 ps.

Single image rain streak removal is a significant and challenging task, which is widely applied in many artificial intelligence domains as preprocessing process. Most of existing rain streak removal works focus on designing various deraining unit (e.g., multi-stream dilation convolution) without considering the correlation between different convolution layers, which may lead to large model size. In this paper, we propose a simple and effective deep network architecture for single image rain streak removal based on deep Convolutional Neural Network (CNN). Benefit from the adjacent layers with different dilation factors have similar feature structures, we design a powerful rain streak representation network based on the Layer Similarity Prior Block (LSPB). To better cater to the property of layer similarity prior, the multi-dense-short-connection is developed and it regards every LSPB as a convolution layer, this connection style makes our overall framework to be a layer similarity prior network. To the best of our knowledge, this is the first paper to investigate the effectiveness of exploiting the layer similarity prior and the multi-dense-short-connection. Quantitative and qualitative experimental results demonstrate that the proposed method outperforms other state-of-the-art methods with the least parameters.

The phenomenon of jitter is examined in image-converter (electro-optical) streak cameras used for studies of high-speed processes. A method for measuring jitter in streak cameras operating in a linear scan mode for the pulsed optical signal is proposed. An experimental apparatus that realizes this measurement technique is described. This apparatus is used at VNIIOFI to check jitter in commercial image-converter streak cameras for compliance with specifications or the technical requirements of customers.

In rainy conditions, imaging devices often capture degraded and blurry images. Most existing works on this problem focus on rain streak removal, but these approaches cannot handle the various types of rain found in images. In this paper, we propose a robust rain removal method for use with single images using an attentive composite residual network. We put forth a single-to-dual encoder-decoder structure, which consists of an attentive net that identifies regions containing rain components during encoding, followed by a dual-channel architecture which recovers the background and detail components of the identified regions during decoding. For the detail subnet, we designed a novel building block, namely a composite residual block (CRB), by constructing multiple residual connections among Res2Net modules. Additionally, we designed another attentive-CRB for the attentive net that uses a squeeze-and-excitation (SE)-Res2Net module, to build a channel-wise attention mechanism. We show that such a deep network can be trained end-to-end from rainy images and that it outperforms the previous state-of-the-art methods on datasets containing different types of rainy images. Experimental results also demonstrate the proposed model's superiority over the competitor's on real rain-affected images, recovering visually clean images and retaining good detail.

The major task of traditional motion deblurring methods is to estimate the blur kernel and restore the latent image. In low-light conditions, the pointolite is likely to produce saturated light streaks in captured blurred images. The light streaks are usually double-edged swords—outliers to the deconvolution, but a cue to kernel estimation. In this paper, we propose a novel blind motion deblurring method for blurred images including light streaks. The main idea is to model the non-linear blur caused by outliers as the Huber's M-estimation in blind deconvolution and take the shape of the light streak as a cue to estimate the blur kernel. Specifically, the optimal light streak patch is selected automatically according to the characteristics of light streaks and the blur kernel. This simple yet effective selection strategy solves the problems of false detection of candidate light streaks and optimal light streak in existing methods. Then, the optimal light streak patch is parameterized as a prior and is combined with other regularizers to estimate the blur kernel. Compared with the state-of-the-art kernel estimation methods, the proposed algorithm reduces the influence of outliers on deconvolution and utilizes more information. Thus, the restored image is more accurate. Experimental results on both synthetic and real images demonstrate the high accuracy of our algorithm.

Maize streak caused by the Maize streak virus (MSV, genus Mastrevirus) is transmitted by Cicadulina spp., and is responsible for considerable maize yield losses in all maize production zones in Africa, including Cameroon. A survey was conducted in 3 agro-ecological zones (AEZ) of Cameroon (Sudano-Sahelian: zone I, Western Highlands: zone III and Bimodal Rainforest: zone V) between November 2017 and November 2019 to determine the status of streak disease in maize farms. The incidence and severity were determined in 90 maize fields, 30 fields per AEZ; the effect of lightning on the disease was also assessed using 15 fields under shade and 15 opened fields per AEZ. The highest streak disease incidence (60%) was found in AEZ I, whereas the lowest incidence was 10% in AEZV. The highest disease incidence and severity (80% and 4.5 respectively) were observed in maize fields under shade as compared to open fields (70% and 4.5 respectively). The phylogenetic analysis of MSV sequences from symptomatic plants indicated it as MSV-A strain identical to be previously reported to determine the virus diversity in relation to the other characterization isolates. This information is important for the development of control strategies to limit yield losses due to MSV.

In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from X-band marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the ES of small-scale wind streaks. The ES of small-scale wind streaks is obtained by establishing an ES scale separation filter. Based on the wind streak characteristics, a 2-D fast Fourier transform is used to obtain the ES of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross section. The proposed algorithm is tested using data collected from X-band radar images and in situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.

Cone beam computed tomography (CBCT) images are degraded by artifacts due to endovascular implants. We evaluated the use of streak metal artifact reduction technique (SMART) in non-contrast CBCT images after endovascular neurosurgery obtained from 148 patients (125 with aneurysm and 23 with dural arteriovenous fistula [dAVF]). Three neurosurgeons evaluated the cistern and brain surface visibility in CBCT images with and without SMART correction based on a 4-point scale (1, excellent; 2, good; 3, limited; and 4, insufficient). Significant improvement in visibility was achieved when the median scores improved from 4 or 3 to 2 or 1 or from 2 to 1. Metal artifact reduction in adjacent slices without metal and new artifacts after SMART correction was also examined. A significant improvement was achieved regarding the visibility of the cistern in 90 (60.8%) images and of the brain surface in 108 (73.0%) images. Metal size (cistern: odds ratio [OR], 0.91 per 1 mm increase; 95% confidence interval [CI], 0.83–0.99), irregular metal shape (cistern: OR, 0.18; 95% CI, 0.05–0.60 and brain surface: OR, 0.15; 95% CI, 0.05–0.45), and infratentorial lesions (cistern: OR, 0.37; 95% CI, 0.14–0.96 and brain surface: OR, 0.30; 95% CI, 0.11–0.80) were negatively correlated with improved visibility. Metal artifact reduction in adjacent slices without metal was obtained in 25.6% and 34.8% of images with aneurysm and dAVF, respectively. New artifacts after SMART correction were found in 4.8% and 13.0% of images with aneurysm and dAVF, respectively. SMART is especially effective for supratentorial small aneurysms.

The official common name of the foliar disease on corn is Diplodia leaf streak, often given the shorthand nomenclature DLS. The only known host of DLS is corn (Zea mays). The fungus that causes DLS is Stenocarpella macrospora (syn. Diplodia macrospora). DLS symptoms can be confused with several common foliar diseases of corn. This guide details symptoms and signs, as well as pathogen identification.

In this paper we formulated and analyzed an optimal deterministic eco-epidemiological model for the dynamics of maize streak virus (MSV) and examine the best strategy to fight maize population from maize streak disease (MSD). The optimal control model is developed with three control interventions, namely prevention (u1), quarantine (u2) and chemical control (u3). To achieve an optimal control strategy, we used the Pontryagin’s maximum principle obtain the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. Numerical simulations are performed using Forward-backward sweep iterative method. The findings show that each integrated strategy is able to mitigate the disease in the specified time. However due to limited resources, it is important to find a cost-effective strategy. Using Incremental Cost-Effectiveness Ratio(ICER) a cost-effectiveness analysis is investigated and determined that the combination of prevention and quarantine is the best cost-effective strategy from the other integrated strategies. Therefore, policymakers and stakeholders should apply the integrated intervention to stop the spread of MSV in the maize population.

This paper proposes a reverse procedure detection method for space target streaks based on motion parameter estimation. According to the phase shift characteristics of the spectrum, the interframe phase difference is used to obtain the displacement vectors of the target streak and image background, and then the morphological parameters of the target streak are estimated. In addition, a streak window is designed on the basis of the parameters, utilizing the local grayscale correlation in two image frames to carry out target searching and positioning with the interval of the target displacement vector. Images from different scenes are used to evaluate the adaptability of our method, and the experimental results suggest that the expected targets can be detected successfully with subpixel precision in all telescope operating modes. The proposed frequency-domain reverse procedure detection method avoids the problem of fake target confirmation caused by blind detection in traditional methods and overcomes the interference produced when the target streak passes through a bright star. Moreover, first target tracking and then positioning, which cannot be performed with time-domain methods, can be achieved. Our proposed method is attractive for multiple applications, such as space target cataloging, early warning and orbit prediction.

Low-dose computed tomography (LDCT) image often contains mottle noise and streak artefacts, which seriously interfere with clinical diagnosis. In this study, the separation-based (SEPB) method is proposed for mottle noise and streak artefacts suppression and structure preservation. In it, the LDCT image is decomposed into the structural image with residual mottle noise and the streak artefacts image with residual structural details by the image decomposition structural-preserving image smoothing method. The structural image is filtered by the K-singular value decomposition algorithm to remove the residual mottle noise, and the structural details in the streak artefacts image are extracted by the morphological component analysis theory. The extracted structural details are added to the filtered structural image to get the LDCT result image. Meanwhile, in the process of extracting the structural details, the streak artefacts dictionary learned from the streak artefacts image is corrected by the local intuitional fuzzy entropy to remove its structural atoms. The experiments are conducted on the modified Shepp–Logan phantom, the pelvis phantom and the clinical abdominal data to evaluate the proposed SEPB method. Compared to several comparative denoising methods, the experimental results show that the SEPB method has better performance in subjective visual effect and objective indicators.