Image Components Research Articles

Satellite Image Encryption using Amalgamation of Randomized Three Chaotic Maps and DNA Encoding

Abstract Image encryption is essential for securing sensitive visual data, protecting privacy, and making certain that only authorized users are able to access the required content. It prevents unauthorized access, tampering, and misuse of images, which is crucial for confidential and secure communications. In fields like healthcare, military, satellite communication, and financial services, encrypted images safeguard against data breaches and cyber threats. As digital images are easily shared and stored, encryption contributes to the integrity maintenance, and privacy of visual data. Motivated by these requirements, we have developed a satellite image encryption scheme that employs a novel 1-D Cosine Sinusoidal Chaotic (1DCSC) map, and two earlier proposed Sine-Tangent Chaotic (STC) and Improved Cosine Fractional Chaotic (ICFCM) maps, in conjunction with Deoxyribonucleic Acid (DNA) operations. The proposed scheme encrypts a given satellite image in four steps. In the initial step, three keys (K1, K2, and K3) are created utilizing a 384-bit shared key and a 128-bit initial vector. In step two, three different chaotic sequences are produced using 1DCSC, STC, and ICFCM maps, which are chosen randomly to encrypt three red, blue or green different components of the given input image. In step three, these three chaotic sequences and the three components of the input image are DNA encoded. In the final step, the green, red, and blue cipher images are created by employing the DNA XOR based diffusion operation between the color components of the DNA-encoded image and DNA encoded chaotic sequences. The proposed scheme obtains entropy value 7.9997, Unified Average Changing Intensity (UACI) value 33.32, and Number of Pixels Change Rate (NPCR) value 99.67.

The Environmental Impact of Iodinated Contrast Media: Strategies for Optimized Use and Recycling.

Iodinated contrast media (ICM) is an integral and ubiquitous component of modern diagnostic imaging. Although most radiology practices are familiar with ICM administration and physiological excretion, they may be less aware of how much ICM is wasted on a per exam basis. Furthermore, radiologists may not recognize the environmental fate of discarded ICM waste. In an evolving world where medical practices are increasingly cognizant of their environmental footprint and radiology practices are considered high consumers of resources, it behooves the radiology community to understand the ICM lifecycle and ways to mitigate unnecessary waste. This review article explains the origin and environmental fate of discarded ICM, with special focus on wastewater contamination. Secondly, the article focuses on feasible options to both optimize use and decrease consumable waste. Specifically, the article addresses ICM vial size inventory diversification, multi-use ICM vials, syringeless contrast injectors, and the potential for using multi-energy imaging (dual-energy or photon counting CT) to accomplish these goals. Finally, the authors share their institutional experience participating in an ICM recycling program and its current departmental impact.

Flexible disk ultramicroelectrode: Facile preparation and high-resolution scanning electrochemical microscopy imaging

BackgroundScanning electrochemical microscopy (SECM) is a kind of scanning probe technology that enables the obtainment of surface morphology and electrochemical information by recording changes in Faraday current triggered by the movement of probe. ResultsIn this work, flexible disk ultramicroelectrode (UME) with highly repeatable geometry are fabricated through a simple and universal strategy that involves vacuum pulling the glass capillaries inserted with platinum wire (gold wire, carbon fiber, etc.), followed by a rapidly heated sealing and polishing process. Based on the optical and electrochemical characterization, the as-prepared UMEs possess favorable geometric shape with a small RG value from 1.7 to 2. Consequently, when applied as probe components for SECM imaging on interdigital electrodes, it can achieve excellent spatial resolution and high sensitivity at short working distances. Moreover, due to the outstanding flexibility, the as-prepared UMEs can be minimally damaged arising from tip-sample collision and produce invisible trace on apple peel after the contact scanning, which is far superior to the traditional UME and suggests the application potential in bio-related fields. SignificanceOur work will inspire the application of flexible UMEs as probe components in SECM, and consequently promote the widespread use of SECM with reduced collision threshold and low operational difficulty.

An efficient method for image denoising based on a new nonlinear wavelet thresholding function

Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the wavelet denoising method works in the transform domain, where the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones.Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of the image and noise components. Another thing: Studies have proven that the thresholding function has an effective role in obtaining better quality of the denoised image. To address these disadvantages, in this paper, we propose an efficient method for image denoising in wavelets domain. This method is based on a new nonlinear wavelet thresholding function, that is characterized by main mathematical properties and a shape parameter. The theoretical study of this new method proves that we can overcome the drawbacks of classical thresholding methods and by freely adjusting the shape parameter, we achieve a compromise between Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to classical thresholding methods in terms of the visual quality of the denoised image.

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Medical imaging is significant for accurate diagnosis, and here, noise often degrades image quality, thus making it challenging to identify important information. Denoising is a component of traditional image pre-processing that helps prevent incorrect disease diagnosis. Mitigating the noise becomes difficult if there are differences in the low-level segment features. Therefore, a Generative Adversarial Network (GAN)-aided Low-Noise Discriminator (LND) is introduced to improve the denoising effectiveness in medical images with a balanced image resolution with noise mitigation. The LND function is a key that distinguishes between high- and low-noise areas based on segmented features, which are also achieved by tuning the peak signal-to-noise ratio (PSNR). Considering the training sequences, the LND-identified intervals lessen the sequences to improve the changes in pixel reconstruction. The generator function in this method is responsible for increasing the PSNR improvements over the different pixels cumulatively. The proposed method successfully improves the pixel reconstruction by 11.05% and PSNR by 9.75%, with 9.75% less reconstruction time and 13.11% less extraction error for the higher pixel distribution ratios than other contemporary methods.

Refined speckle contrast estimation in OCT based on compensation of scattering-related distortions of speckle pattern parameters

Abstract Speckle contrast (SC) parameters in optical coherence tomography (OCT) scans are formed by the interplay of several factors—local level of optical wave backscattering by the material inhomogeneities, parameters of spatial distribution of the latter and the degree of cumulative optical wave attenuation during its fourth-and-back propagation. For the optical wavelengths used in OCT, this attenuation is usually dominated by the influence of scattering in the visualized turbid tissues rather than by absorption. For sufficiently high concentrations of scatterers (at least a few scatterers in the coherence volume) characterized by comparable scattering strengths and a fairly homogeneous distribution in space, the interference of locally scattered waves should be characterized by a Rayleigh distribution of speckle amplitudes, for which the local SC tends to 0.52. Equivalently, in terms of speckle intensities, the local SC tends to unity. Local spatial inhomogeneities or strongly uneven scattering strengths lead to the appearance of increased values of SC, which may serve as a diagnostic feature of some tissue components in OCT images. At the same time, in comparison to surface speckles formed by coherent-light scattering from rough surfaces, OCT-beam attenuation during its fourth-and-back propagation may introduce intensity inhomogeneities in OCT scans even for homogeneous tissue areas. This effect results in artefactual distortion of the visible SC in comparison with the above-mentioned expected value. Moreover, the presence of individual strong scatterers may additionally non-locally distort SC values due to the appearance of elongated shadows below such scatterers, which causes lateral inhomogeneities in OCT scans. Here, we propose a refined SC parameter, which is cleaned from distorting scattering-related effects in both axial and lateral directions. Depth-resolved estimation of the optical attenuation coefficient is used to restore attenuation-free OCT scans, for which the refined SC is estimated. The efficiency of the proposed approach is demonstrated using both digital OCT phantoms with highly controlled properties and experimental OCT data.

A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging. This study aims to develop a wavelet-based DL method capable of restoring high-quality imaging from ultra-low-dose PET scans. In contrast to conventional DL techniques that denoise images in the spatial domain, we introduce WaveNet, a novel approach that inputs wavelet-decomposed frequency components of PET imaging to perform denoising in the frequency domain. A dataset comprising total-body 18F -FDG PET images of 1447, acquired using total-body PET scanners including Biograph Vision Quadra (Siemens Healthineers) and uEXPLORER (United Imaging) in Bern and Shanghai, was utilized for developing and testing the proposed method. The quality of enhanced images was assessed using a customized scoring system, which incorporated weighted global physical metrics and local indices. Our proposed WaveNet consistently outperforms the baseline UNet model across all levels of dose reduction factors (DRF), with greater improvements observed as image quality decreases. Statistical analysis (p < 0.05) and visual inspection validated the superiority of WaveNet. Moreover, WaveNet demonstrated superior generalizability when applied to two cross-scanner datasets (p < 0.05). WaveNet developed with total-body PET scanners may offer a computational-friendly and robust approach to recover image quality from ultra-low-dose PET imaging. Its adoption may enhance the reliability and clinical acceptance of DL-based dose reduction techniques.

Rapid post-earthquake damage assessment of building portfolios through deep learning-based component-level image recognition

Frequent seismic events significantly heighten the likelihood of the building structure being impacted throughout its operational lifecycle. Seismic effects on these structures result in a notable reduction in structural safety and serviceability. Enabling the post-earthquake recovery of building structures necessitates a precise and swift assessment of earthquake-induced damage. The conventional method for assessing building damage involves collecting data through close manual observation or contact inspection. Although it can obtain relatively accurate results by combining evaluation specifications, it is time-consuming and labour-intensive. Deep learning (DL) is data-driven and employs computational methods for data processing. The image classification function of convolutional neural network (CNN) in DL brings great convenience to image data processing. It has been applied to various aspects of structural health monitoring/post-disaster assessment. However, most of the current studies focus on the component level and lack of a comprehensive perspective on the whole structure, which is not conducive to the judgment of the overall condition. Consequently, this paper proposes a rapid assessment method of the overall damage level of post-earthquake buildings based on component images and DL. Two component-level image classification models, component type and damage level, are firstly trained. Then, the images of the buildings to be evaluated after the earthquake are collected and classified. Combined with the weights of different component types and damage levels proposed, the overall condition scores and grades of the structures can be obtained by weighted calculation. The proposed method realizes an overall evaluation of the structural damage condition after seismic events, and further extends to the building portfolios, providing actionable guidance for subsequent personnel and fund allocation, rescue, and maintenance measures. Due to limitations in the dataset, such as the potential biases in the training dataset, the trained model is not perfect and faces challenges in distinguishing between minor and moderate damage. In the future, the dataset should update and ideally cover a wide range of building types, component types, and damage levels to ensure the model's robustness and applicability to various real-world scenarios.

Aberrant intra-network resting-state functional connectivity in chronic insomnia with or without cognitive impairment

Chronic insomnia (CI) is a common sleep disorder in middle-aged and elderly individuals. Long-term sleep deprivation can lead to physical, mental, and cognitive damage. Resting-state networks (RSNs) in the brain are closely linked to cognition and behavior. Therefore, we investigated changes in RSNs to explore behavioral and cognitive abnormalities in middle-aged and elderly CI patients. Resting state functional magnetic resonance imaging (rs-fMRI) and independent component analysis were used to study the intrinsic functional connectivity (FC) of the RSNs in 36 CI patients (20 CI with cognitive impairment (CI-I) patients and 16 CI without cognitive impairment (CI-N) patients) and 20 healthy controls (HC). Two-sample t-tests were used to compare RSNs differences between CI and HC groups and the RSNs differences between CI-I and CI-N groups. Partial correlation analysis was used to explore the relationship between the significant abnormal brain regions in RSN and clinical scales. Compared with HCs, CI patients showed significant differences in multiple RSNs, and FC values in two brain regions within RSNs were correlated with clinical scales. Furthermore, compared with CI-N group, CI-I group also showed significantly altered FC in multiple RSNs. Moreover, FC values in the right middle frontal gyrus within right frontal parietal network of CI-I patients were negatively correlated with the Mini-Mental State Examination scores. These results may explain hyperarousal, decreased attention and motor function impairments in CI patients. Furthermore, the aberrant alterations of RSNs in CI-I patients may play a crucial role in the onset and progression of cognitive impairment in CI patients.

Digital image processing to detect adaptive evolution.

In recent years, advances in image processing and machine learning have fueled a paradigm shift in detecting genomic regions under natural selection. Early machine learning techniques employed population-genetic summary statistics as features, which focus on specific genomic patterns expected by adaptive and neutral processes. Though such engineered features are important when training data is limited, the ease at which simulated data can now be generated has led to the recent development of approaches that take in image representations of haplotype alignments and automatically extract important features using convolutional neural networks. Digital image processing methods termed α-molecules are a class of techniques for multi-scale representation of objects that can extract a diverse set of features from images. One such α-molecule method, termed wavelet decomposition, lends greater control over high-frequency components of images. Another α-molecule method, termed curvelet decomposition, is an extension of the wavelet concept that considers events occurring along curves within images. We show that application of these α-molecule techniques to extract features from image representations of haplotype alignments yield high true positive rate and accuracy to detect hard and soft selective sweep signatures from genomic data with both linear and nonlinear machine learning classifiers. Moreover, we find that such models are easy to visualize and interpret, with performance rivaling those of contemporary deep learning approaches for detecting sweeps.

CNN-based glioma detection in MRI: A deep learning approach.

More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is still a significant difficulty in clinical settings, despite improvements in Magnetic Resonance Imaging (MRI) and diagnostic tools. Convolutional neural networks (CNNs) have seen recent advancements that offer promise for increasing segmentation accuracy, addressing the pressing need for improved diagnostic and therapeutic approaches. The study intended to develop an automated glioma segmentation algorithm using CNN to accurately identify tumor components in MRI images. The goal was to match the accuracy of experienced radiologists with commercial instruments, hence improving diagnostic precision and quantification. 285 MRI scans of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were analyzed in the study. T1-weighted sequences were utilised for segmentation both pre-and post-contrast agent administration, along with T2-weighted sequences (with and without Fluid Attenuation by Inversion Recovery [FAIRE]). The segmentation performance was assessed with a U-Net network, renowned for its efficacy in medical image segmentation. DICE coefficients were computed for the tumour core with contrast enhancement, the entire tumour, and the tumour nucleus without contrast enhancement. The U-Net network produced DICE values of 0.7331 for the tumour core with contrast enhancement, 0.8624 for the total tumour, and 0.7267 for the tumour nucleus without contrast enhancement. The results align with previous studies, demonstrating segmentation accuracy on par with professional radiologists and commercially accessible segmentation tools. The study developed a CNN-based automated segmentation system for gliomas, achieving high accuracy in recognising glioma components in MRI images. The results confirm the ability of CNNs to enhance the accuracy of brain tumour diagnoses, suggesting a promising avenue for future research in medical imaging and diagnostics. This advancement is expected to improve diagnostic processes for clinicians and patients by providing more precise and quantitative results.

Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations

This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.

Color image watermarking using vector SNCM-HMT

An image watermarking scheme is typically evaluated using three main conflicting characteristics: imperceptibility, robustness, and capacity. Developing a good image watermarking method is challenging because it requires a trade-off between these three basic characteristics. In this paper, we proposed a statistical color image watermarking based on robust discrete nonseparable Shearlet transform (DNST)-fast quaternion generic polar complex exponential transform (FQGPCET) magnitude and vector skew-normal-Cauchy mixtures (SNCM)-hidden Markov tree (HMT). The proposed watermarking system consists of two main parts: watermark inserting and watermark extraction. In watermark inserting, we first perform DNST on R, G, and B components of color host image, respectively. We then compute block FQGPCET of DNST domain color components, and embed watermark signal in DNST-FQGPCET magnitudes using multiplicative approach. In watermark extraction, we first analyze the robustness and statistical characteristics of local DNST-FQGPCET magnitudes of color image. We then observe that, vector SNCM-HMT model can capture accurately the marginal distribution and multiple strong dependencies of local DNST-FQGPCET magnitudes. Meanwhile, vector SNCM-HMT parameters can be computed effectively using variational expectation–maximization (VEM) parameter estimation. Motivated by our modeling results, we finally develop a new statistical color image watermark decoder based on vector SNCM-HMT and maximum likelihood (ML) decision rule. Experimental results on extensive test images demonstrate that the proposed statistical color image watermarking provides a performance better than that of most of the state-of-the-art statistical methods and some deep learning approaches recently proposed in the literature.

A hidden active galactic nucleus population: the first radio luminosity functions constructed by physical process

ABSTRACT Both star formation (SF) and active galactic nuclei (AGNs) play an important role in galaxy evolution. Statistically quantifying their relative importance can be done using radio luminosity functions (RLFs). Until now these relied on galaxy classifications, where sources with a mixture of radio emission from SF and AGN are labelled as either a star-forming galaxy or an AGN. This can cause the misestimation of the relevance of AGN. Brightness temperature measurements at 144 MHz with the International LOw Frequency ARray telescope can separate radio emission from AGN and SF. We use the combination of sub-arcsec and arcsec resolution imaging of 7497 sources in the Lockman Hole and ELAIS-N1 fields to identify AGN components in the sub-arcsec resolution images and subtract them from the total flux density, leaving flux density from SF only. We construct, for the first time, RLFs by physical process, either SF or AGN activity, revealing a hidden AGN population at $L_{\textrm {144 MHz}}$$\lt 10^{24}$ W Hz$^{-1}$. This population is 1.56 $\pm$ 0.06 more than expected for $0.5\lt z\lt 2.0$ when comparing to RLFs by galaxy classification. The star-forming population has only 0.90 $\pm$ 0.02 of the expected SF. These ‘hidden’ AGNs can have significant implications for the cosmic SF rate and kinetic luminosity densities.

Body image facets as predictors of muscularity-oriented disordered eating in women: Findings from a prospective study

Muscularity-oriented disordered eating (MODE) is becoming increasingly common among women and is characterized by dietary alterations (e.g., blending meals into liquid form to increase caloric intake) aimed towards gaining lean muscle. In light of the mental health risks associated with these pathological eating behaviors, understanding factors that influence women’s engagement in MODE is essential for preventative efforts and for informing etiological models. Body image is a possible factor that may influence MODE in light of evidence of cross-sectional associations and its importance as a key risk factor for thinness-oriented disordered eating. However, research is yet to test for prospective relationships between the various components of body image (i.e., body dissatisfaction, body appreciation) and MODE, which was the aim of this study. Adult women completed the online study measures at baseline (Time 1 [T1]; n = 1760) and three-month follow-up (Time 2 [T2]; n = 1208). A series of univariate regressions revealed that all body image facets (i.e., preoccupation, overvaluation, dissatisfaction, body image appreciation and feeling fat) at T1 significantly predicted MODE at T2. However, multivariable models revealed that only preoccupation and body appreciation uniquely predicted MODE at T2, with a positive relationship observed for preoccupation and a negative one for body appreciation. This is the first study to establish temporal relationships between distinct body image facets and MODE. Pending replication, findings highlight possible targets for addressing MODE in women.

Research on Grading Method for Jin Guan Apples based on Machine Vision

As the demand for apples increases, so do the expectations for apple quality. To address the time-consuming and labor-intensive nature of apple grading, this paper focuses on the Jin-Guan apple variety and applies machine vision techniques to extract three key features: maximum transverse diameter, shape index, and surface color. A backpropagation neural network (BP neural network) is employed to achieve accurate grading of the apples. First, by analyzing the relationships between the RGB color space components in the original images, the apple images are extracted using the method of comparing the R channel value to the B channel value. Gaussian filtering is then applied to remove image noise. Second, the complete apple contour is extracted using an improved Sobel operator method. The maximum transverse diameter and shape index of the apple are calculated based on the pixel coordinates along the contour, and the apple's surface color is determined using a nearest-neighbor classifier. Finally, a BP neural network model is established and trained to perform the grading. A total of 160 apples were used for training, and 140 for testing. The results show that the grading accuracy reached 92.14%, and the grading efficiency was approximately 1.7 times that of a skilled human grader. This grading method provides a technical reference for apple grading processes.

A theoretical analysis of the logging-while-drilling dipole acoustic reflection measurement

Post-drilling wireline acoustic single-well imaging technology can now detect geological structures tens of meters away from boreholes. Further development of this single-well imaging technology in the logging-while-drilling (LWD) environment will have significant values in real-time applications such as geosteering and reservoir navigation. Based on the wireline imaging application, we propose a new method for the LWD application. In wireline imaging, the four-component (4C) dipole acoustic data are azimuthally rotated to scan the reflectors around the borehole. In LWD, azimuthal scanning is achieved by drilling rotation such that the 4C dipole system in the wireline is replaced by a one-dipole-source and two-receiver LWD system, where the two receivers are mounted on opposite sides of the drill collar. For the LWD application, we first developed the theory for LWD dipole shear-wave reflection imaging and validated the theory using 3D finite-difference waveform modeling. Using the analytical solution, we analyzed the far-field radiation directivity of an acoustic LWD dipole source and the effect of drilling rotation on the shear-wave reflection imaging using the LWD acoustic system. The LWD analysis results show that, for fast formations, the SH-wave is the dominant component for imaging, whereas for slow formations, the P-wave becomes important and can be used for imaging. Our results also indicate that the reflection data acquired by the system are affected by the speed of drilling rotation. The take-off azimuth at the wave radiation may be different from the incident azimuth at the wave reception. Knowing the rotation speed, this azimuth difference can be corrected. A further advantage of using the oppositely mounted receivers is that the reflected wave arrives earlier (later) at the front (back)-side receiver; thus, the arrival time difference between the receivers can be used to eliminate the 180°-azimuth ambiguity of dipole acoustic imaging. For reflection imaging, using the proposed LWD system configuration, we tested its azimuth sensitivity and validated its 180°-ambiguity solution using synthetic LWD and field wireline dipole data. The results of this work, therefore, provide a theoretical foundation for the development of the LWD acoustic reflection imaging system.

WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

Identification of weeds in cotton fields at various growth stages using color feature techniques

Weeds affect the growth and yield of cotton (Gossypium hirsutum), which is the most prevalent commercially viable non-food crop. Accurate differentiation between weeds and cotton plants is crucial for implementing precise weed-control strategies, optimizing crop yields, and minimizing herbicide usage. In this study, we aimed to investigate the effectiveness of using color feature techniques combined with Otsu's method (OTSU) to distinguish weeds from cotton plants at various growth stages. The results revealed that the image segmentation approach achieved a recognition rate of 74.1 % during the second growth stage. In the third growth stage, the weed identification process primarily targeted lambsquarters (Chenopodium album). The standard deviation (SD) difference between the red (R), green (G), and blue (B) components of the plant images revealed that the difference between the SD values of R and B was <5, which could serve as an effective threshold for identifying lambsquarters. Additionally, the recognition rates for cotton and lambsquarters were 71.4 % and 92.9 %, respectively, and the overall recognition rate reached 82.1 % during the identification process. The results of this study have practical implications for weed management practices, such as targeted herbicide application, mitigation of environmental impacts, and contribution to higher crop yields. Future research will focus on refining these methods by exploring advanced image processing techniques that integrate deep learning for automatic feature extraction, which will be evaluated for handling complex agronomic scenarios, such as leaf occlusion, varying growth stages, and image noise.

Nitric Oxide-Photodelivering Materials with Multiple Functionalities: From Rational Design to Therapeutic Applications.

The achievement of materials that are able to release therapeutic agents under the control of light stimuli to improve therapeutic efficacy is a significant challenge in health care. Nitric oxide (NO) is one of the most studied molecules in the fascinating realm of biomedical sciences, not only for its crucial role as a gaseous signaling molecule in the human body but also for its great potential as an unconventional therapeutic in a variety of diseases including cancer, bacterial and viral infections, and neurodegeneration. Handling difficulties due to its gaseous nature, reduced region of action due to its short half-life, and strict dependence of the biological effects on its concentration and generation site are critical questions to be solved for appropriate therapeutic uses of NO. Light-activatable NO precursors, namely, NO photodonors (NOPDs), address the above issues since they are stable in the dark and permit in a noninvasive fashion the remote-controlled delivery of NO on demand with great spatiotemporal precision. Engineering biocompatible materials with NOPDs and their combination with additional imaging, therapeutic, and phototherapeutic components leads to intriguing light-responsive multifunctional constructs exhibiting promising potential for biomedical applications. This contribution illustrates the most significant progress made over the last five years in achieving engineered materials including nanoparticles, gels, and thin films, sharing the common feature to deliver NO under the exclusive control of the biocompatible visible/near infrared light inputs. We will highlight the logical design behind the fabrication of these systems, illustrating the potential therapeutic applications with particular emphasis on cancer and bacterial infections.