A novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study, aimed at improving both the accuracy robustness of detection. The integrates image sensor data, optimizing features through an embedded mechanism, which enhances model’s ability to focus disease-related features. Experimental results demonstrate that excels across various evaluation metrics, achieving a precision 0.94, recall 0.90, 0.92, mAP@75 0.91, indicating...

Centerline extraction is the basic and key procedure in line-structured laser 3-D scanners. In this article, we propose a hardware-oriented algorithm for fast accurate of centerline based on Hessian matrix. The divided into three low-coupling modules that can be processed parallel - coarse positioning, linewidth estimation, precise positioning module. module, window slider used to traverse all image pixels raster-scan mode collecting local features, which potential region interest (ROI)...

The interscale Stein's unbiased risk estimator (SURE)-based approach introduced by Luisier is a recent state of the art in orthonormal wavelet denoising, but it not very effective for those images that have substantial high-frequency contents. To solve this problem, we introduce an integration intrascale correlations within SURE-based approach. We show consideration both and dependencies coefficients brings more denoising gains than obtained with approach, especially textures such as Barbara image.

We present a superresolution imaging method based on the dynamic single-pixel compressive sensing (CS) system. Different from traditional static CS, this system is slowly moving in parallel with scene during sampling, implying that measurements are possible to contain information about subpixel resolution. Here we first build sampling model and give recovery via CS scheme, then propose image framework, where subdivision scheme used. The proposed not only has remarkable performance, but also...

In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is great importance. Traditional disease methods show limited performance in complex environments, making it challenging to meet the demands for modern agriculture. This paper proposes a model based on state-space attention mechanism, aiming effectively utilize spatiotemporal characteristics achieve efficient accurate detection. The introduces mechanism combined with multi-scale feature...

A new non-training complex wavelet hidden Markov tree (HMT) model, which is based on the dual-tree transform and a fast parameter estimation technique, proposed for image denoising. This model can mitigate two problems (high computational cost shift-variance) of conventional HMT simultaneously. Experiments show that denoising approach with this achieves better performance than other related HMT-based algorithms.

The physical imaging model, which is based on atmospheric absorption and scattering, plays an important role in single-image dehazing. It critical that the transmission accurately estimated for dehazing algorithm model. A self-adaptive weighted least squares (AWLS) model proposed to refine rough transmission, extracted by dark channel (DC) In our gray-world hypothesis a smoothing technique with edge preservation are integrated optimize remove artifacts brought DC self-AWLS has higher...

This study proposes two adaptive vectorial total variation models for multi‐channel synthetic aperture radar (SAR) images despeckling with the help of prior knowledge image amplitude. Besides SAR efficiently, proposed new have advantages over other methods in many aspects, such as preserving reflectivity, targets and edges contrast. The Bermudez‐Moreno algorithm accelerated fast iterative shrinkage thresholding are employed to implement models, respectively. Experimental results on...

Reliability has become one of the most crucial issues in network‐on‐chip (NoC). But how to keep low latency and power consumption when achieving reliability is still a curial challenge. A novel scheme for reliable NoCs proposed. In scheme, header flit protected from router data end end. To implement new protection buffer routers designed, which can check timing errors tolerate soft simultaneously. Instead checking on each router, packet only decoded checked receiver's network interface. this...

A new family of dual-tree based dyadic complex (approximate analytic) wavelet tight frames that is an extension higher density discrete transform (DWT) introduced by Selesnick (in Higher-density DWT, IEEE Transaction on Signal Processing) proposed. Because the proposed are good combinations higher-density DWT and dual tree transform, corresponding can be potentially applied to applications when properties both transforms required simultaneously. The design problem obtain finite impulse...

In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks discover evolutionary conserved substructures at the system level. However, most previous methods aim maximize similarity of aligned proteins in pairwise networks, while concerning little about feature connectivity these substructures, such as protein complexes.In this paper, we identify problem finding complexes, which requires a PPI form connected subnetwork. By taking into...

In multichannel image processing, many tasks, such as denoising, deblurring, and inpainting, can be included in the fields of inverse problems high-dimensional data processing. To get better performance, there are two key points that should considered seriously. On one hand, most these ill-posed, prior constraints necessary to transform them into well-posed by reducing solution space. other information coupling among different channels is important where each channel usually processed...

Traditional SVM (support vector machine) multi-class classification methods are mainly based on one-to-one and one-to-multi, which both have disadvantages in applications: slow computational speed low precision. This paper introduces a new method error correcting code to reduce the training time improve In view of relations among length, Hsmming distance order generalization ability each SVM, we propose principles table-designing center-range that ascertains eliminate problem caused by...

This paper presents a new Synthetic Aperture Radar (SAR) imaging system based on compressive sampling scheme and $l_1$ minimization. The comprises of randomization integration radar echoes which slows down the analog-to-digital converters (ADC) rate significantly without an aliasing in image formation. Numerical experiments indicate that resolution SAR images retrieved by our method outperform obtained conventional methods. results also reveal can still retrieve non-ambiguous even when data...

Target recognition based on 2D radar profile is a rising application of technology, because can offer more structural information about targets. But the methods feature extraction are very scarce. In this paper, new approach double-sides 2DPCA presented. It performed by using original image matrices directly, while PCA always needs to be transformed into 1D vector. Besides, it reduce dimension from two sides and obtain with much smaller size, traditional 2DPCA, mainly used in area face...

The problem of image restoration has been extensively studied for its practical importance and theoretical interest. This paper mainly discusses the with partly unknown kernel. In this model, degraded kernel function is known but parameters are unknown. With we should estimate in Gaussian real simultaneity. For new problem, a total variation model put out an intersect direction iteration algorithm designed. Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measurement (SSIM) used...

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging constructing an anatomy ensemble these models, and a dichotomous object recognition-delineation process. parent-to-offspring spatial relationship in the hierarchy is crucial AAR method. We have found this to be quite complex, as such any improvement capturing information model will improve process recognition itself. Currently, method...

Coded aperture snapshot spectral imager (CASSI) has been a popular imaging architecture for its ability of capturing hyperspectral images with high temporal resolution. However, such system entails large sacrifice in the spatial resolution data cube, since only small amount light gets into during one snapshot. Also, CASSI is limited by pixel size (and amount) detector, while it difficult to fabricate dense detector size, especially infrared bands. Super-resolution an advanced post-processing...

The progressive compressive imaging by single-pixel imager is presented in this paper. We aim to offer the online control of tradeoff between sampling rate and quality recovered image avoid need a priori knowledge object sparsity. Moreover, we can implement proposed approach innovation method reduce computation complexity memory requirements as well improving recovery precision. also show stopping rule finite truncation for method. results numerical simulations feasibility