Improve Image Quality Research Articles

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results.

Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method. The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions. Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions. Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT.

Depth-resolved imaging through scattering media using time-gated light field tomography.

We present a novel, to the best of our knowledge, approach to overcome the limitations imposed by scattering media using time-gated light field tomography. By integrating the time-gating technique with light field imaging, we demonstrate the ability to capture and reconstruct images with different depths through highly scattering environments. Our method exploits the temporal characteristics of light propagation to selectively isolate ballistic photons, enabling enhanced depth resolution and improved imaging quality. Through comprehensive experimental validation and analysis, we showcase the effectiveness of our technique in resolving depth information with high fidelity, even in the presence of significant scattering. The resultant system can simultaneously acquire multi-angled projections of the object without requiring prior knowledge of the media or the target. This advancement holds promise for a wide range of applications, including non-invasive medical imaging, environmental monitoring, and industrial inspection, where imaging through scattering media is critical for an accurate and reliable analysis.

Evaluating Factors Affecting Mean Glandular Dose in Mammography: Insights from a Retrospective Study in Dubai

Background/Objective: This study evaluates the mean glandular dose (MGD) in mammography screening for women aged 40–69 in Dubai, based on a retrospective analysis of a dose survey involving 2599 participants. Methods: MGD was calculated using the Dance formula. Results: The average MGD was 0.96 ± 0.39 mGy for mediolateral oblique (MLO) views and 0.81 ± 0.33 mGy for craniocaudal (CC) views. Weak inverse correlations were found between age and organ dose (OD) for both views, while a direct relationship was observed between breast thickness and entrance skin dose (ESD). In adjusted models, ESD was strongly associated with MGD (β = 1.04, 95% CI: 0.97, 1.09), while OD showed a moderate association (β = 0.44, 95% CI: 0.40, 0.49). Significant variations in ESD, OD, and MGD were noted across age groups and breast thicknesses. Conclusions: Lower MGD indicates reduced radiation exposure risk, while higher MGD in MLO views suggests improved imaging quality. Monitoring and optimizing MGD are essential for enhancing patient safety and screening efficacy.

Dental Implant Artifacts in MRI: Compatibility and Considerations

This review investigated dental implant artifacts in magnetic resonance imaging (MRI) and their safety in clinical practice. Dental prostheses, including implants, crowns, and orthodontic appliances, cause artifacts due to their high magnetic susceptibility, particularly in materials like iron, stainless steel, and cobalt-chromium. Titanium implants are considered safe under MRI environments according to the American Society for Testing and Material (ASTM) standards, with no reported thermal injury or dislodgement during examinations. Despite limited artifacts from titanium's paramagnetic nature, minute ferromagnetic components can still affect visualization. Thus, reducing artifacts in oral and maxillofacial MRI scans is crucial. Two main categories of artifact reduction techniques are identified: improved porous titanium or alternative materials like zirconia and adjusting MR parameters with advanced sequences. Recommendations include increasing the readout bandwidth, reducing slice thickness, using spin-echo sequences instead of gradient-echo, and employing short tau inversion recovery or DIXON techniques for fat suppression. Additional methods like VAT, VAT-SEMAC combination, and MAVRIC show promise, although applicability may be restricted in specific MRI scanners. Continuous advancements in dental implant materials and MRI sequences are needed to improve imaging quality and reduce artifacts. Collaboration among dental practitioners, radiologists, and MRI technologists is essential for refining techniques and ensuring patient safety. Although overall dental implant artifacts pose challenges, safety in MRI is well-established. Ongoing developments hold significant potential to enhance MRI imaging quality in patients with dental devices

The optimal energy level of virtual monochromatic imaging in Dual-energy CT arthrography of the wrist.

To suggest an optimal energy level of virtual monochromatic images (VMIs) in dual-energy CT arthrography of the wrist. This retrospective study included 53 patients with wrist CT arthrography. Conventional polychromatic images and VMIs at four energy levels (40 to 70 keV at 10 keV intervals) were obtained. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured, and qualitative analysis of image quality and diagnostic confidence was performed. For each patient, an energy level with the best image quality was chosen by consensus. Comparisons of quantitative and qualitative parameters between VMI sets were performed. The image noise of bone and muscle were increased with decreasing energy level (p < 0.001). The noise of contrast was highest on 60 keV VMI. SNR and CNR (between contrast and muscle) were increased with decreasing energy level and were markedly increased between 60 and 50 keV (p < 0.001). The 60 keV VMI demonstrated the highest image quality and diagnostic confidence, chosen as the best diagnostic image (n = 31/53). Given that the attenuation of the contrast material was low on the conventional image, the optimal energy level of the best VMI tended to be low. Wrist dual-energy CT arthrography with VMIs at 60 keV or less could improve image quality and diagnostic performance by increasing SNR and CNR in cases with low contrast attenuation. Wrist dual-energy CT arthrography with VMIs at variable keV could be utilised to enhance SNR and CNR, thereby achieving diagnostic images of high quality.

Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.

To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms. This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale. SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR (P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers (P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures (P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures (P < .05). SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.

Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.

The imaging of inflammatory myelopathies has advanced significantly over time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D double inversion recovery (DIR) imaging for inflammatory myelopathies remains unassessed. This study aims to compare acquisition time, image quality, diagnostic confidence, and lesion detection rates among sagittal T2WI, standard DIR, and DL -reconstructed DIR in patients with inflammatory myelopathies. In this observational study, patients diagnosed with inflammatory myelopathies were recruited between June 2023 and March 2024. Each patient underwent sagittal conventional turbo spin-echo sequences and standard 3D DIR (T2WI and standard 3D DIR used as references for comparison), followed by an undersampled accelerated DIRDL examination. Three neuroradiologists evaluated the images using a four-point Likert scale (from 1 to 4) for overall image quality, perceived signal-tonoise ratio, sharpness, artifacts, and diagnostic confidence. The acquisition times, and lesion detection rates were also compared between the acquisition protocols. A total of 149 participants were evaluated (mean age 40.6 ± 16.8 years; 71 females). The median acquisition time for DIRDL was significantly lower than for standard DIR (298 seconds [interquartile range (IQR), 288-301] vs 151 seconds [IQR, 148-155]; P < 0.001), showing a 49%time reduction. DIRDL images scored higher in overall quality, perceived signal-to-noise ratio, and artifact noise reduction (all P < 0.001). There were no significant differences in sharpness (P = 0.07), or diagnostic confidence (P = 0.06) between the standard DIR and DIRDL protocols. Additionally, DIRDL detected 37% more lesions compared to T2WI (300 vs. 219; P < 0.001). DIRDL significantly reduces acquisition time and improves image quality compared to standard DIR without compromising diagnostic confidence. Additionally, DIRDL enhances lesion detection in patients with inflammatory myelopathies, making it a valuable tool in clinical practice. These findings underscore the potential for incorporating DIRDL into future imaging guidelines. DL = deep learning; DIR = double inversion recovery; IQR = interquartile range; MS = multiple sclerosis; AQP4+NMOSD = AQP4-IgG positive neuromyelitis optica spectrum disorders; MOG = myelin oligodendrocyte glycoprotein; MOGAD = MOG antibody-associated diseases.

Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen.

To evaluate the feasibility of a high-precision single-shot fast spin-echo (SS-FSE) sequence using the deep learning-based Precise IQ Engine (PIQE) algorithm in comparison with standard SS-FSE for T2-weighted MR imaging of the abdomen, and to compare the image quality with a multi-shot (MS)-FSE sequence using the PIQE algorithm. This retrospective study included 105 patients who underwent abdominal MR including T2-weighted sequences using the PIQE reconstruction algorithm. The image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in high-precision SS-FSE sequences using PIQE were compared to those in standard SS-FSE without PIQE and MS-FSE sequences using PIQE. The scores for all qualitative parameters were significantly higher in high-precision SS-FSE sequence using PIQE than in standard SS-FSE sequence without PIQE (all p < 0.001). In the comparison between two high-precision sequences using PIQE, the SS-FSE sequence showed significantly better scores for the blurring, ghosts or motion/flow artifacts, conspicuity of intrahepatic structures, focal nonsolid hepatic and pancreatic cystic lesions, and overall image quality, in comparison to the MS-FSE sequence (all p < 0.001). Additionally, the SS-FSE sequence using PIQE showed significantly higher SNR of the liver and CNR of nonsolid hepatic lesions than the MS-FSE sequence using PIQE (p < 0.001). A high-precision SS-FSE sequence using the PIQE algorithm is a feasible alternative to the standard FSE sequence in T2-weighted MR imaging of the abdomen. It can improve image quality, the SNR of the liver, and the ability to visualize nonsolid focal liver lesions and pancreatic cystic lesions in comparison to a high-precision MS-FSE sequence using PIQE although this study was limited by single-center design and lack of pathological confirmation.

Hierarchical Image Quality Improvement Based on Illumination, Resolution, and Noise Factors for Improving Object Detection

Object detection performance is significantly impacted by image quality factors such as illumination, resolution, and noise. This paper proposes a hierarchical image quality improvement process that dynamically prioritizes these factors based on severity, enhancing detection accuracy in diverse conditions. The process evaluates each factor—illumination, resolution, and noise—using discriminators that analyze brightness, edge strength, and noise levels. Improvements are applied iteratively with an adaptive weight update mechanism that adjusts factor importance based on improvement effectiveness. Following each improvement, a quality assessment is conducted, updating weights to fine-tune subsequent adjustments. This allows the process to learn optimal parameters for varying conditions, enhancing adaptability. The image improved through the proposed process shows improved quality through quality index (PSNR, SSIM) evaluation, and the object detection accuracy is significantly improved when the performance is measured using deep learning models called YOLOv8 and RT-DETR. The detection rate is improved by 7% for the ‘Bottle’ object in a high-light environment, and by 4% and 2.5% for the ‘Bicycle’ and ‘Car’ objects in a low-light environment, respectively. Additionally, segmentation accuracy saw a 9.45% gain, supporting the effectiveness of this method in real-world applications.

Impact of pre-examination video education in Gd-EOB-DTPA-enhanced liver MRI: A comparative study.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and early diagnosis via gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre-examination video education on anxiety, satisfaction and image quality in Gd-EOB-DTPA-enhanced liver MRI. We prospectively enrolled 480 patients who underwent Gd-EOB-DTPA-enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups. The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre-contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05). Supplementing routine pre-scan care with video guidance for Gd-EOB-DTPA-enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video-based interventions to enhance the MRI experience for patients.

Randomizing polarization of displays for fundamental improvement of color mura caused by birefringence.

Most existing displays utilize polarization technologies to produce images and improve image quality. However, polarized light from displays causes color mura because of the birefringence of the polymer films used. Thus, eliminating color degradation remains a challenge despite the incorporation of complex polarization technologies such as retardation films. Our proposed random depolarization film (RDF) addresses this issue by randomizing the polarization of light from displays. Chromaticity measurements demonstrate that the RDF effectively compensates for color mura due to low-cost polymer films. Notably, the RDF compensation mechanism is independent of the RDF position and light source spectrum. Therefore, the RDF could be an innovative solution for color degradation in existing displays.

Influencing factors of self-aligned imaging in near-field lithography for the Fabry–Perot cavity effect

In near-field lithography, the Fabry–Perot (F-P) cavity enhancement effect can significantly improve image quality and resolution. This paper considers changes in the refractive index and air distance in self-aligned imaging. Simulation results demonstrate that the Fabry–Perot resonator effect achieves effective self-alignment in 3D imaging. The proposed structure builds on traditional near-field imaging structures and F-P resonator research, suggesting a Cu/SiO2 structure as the front layer. Rigorous coupled wave analysis (RCWA), finite element method (FEM), and finite-difference time-domain (FDTD) methods were employed to verify the self-alignment effect on single gratings and rectangular hole arrays. The results indicate that the self-alignment lithography method based on the F-P effect not only enhances lithography contrast and normalized image log-slope (NILS) but also shows robust performance against variations in air distance and complex refractive index. Notably, for the rectangular aperture array structure, with changes in air distance and complex refractive index within a certain range, the NILS remains stable above 2.8, and the contrast stays near 0.70. These simulation results confirm that the F-P resonator-based scheme is viable for plasma imaging lithography with small critical dimensions.

Enhancing Image Dehazing Efficiency with Dual Colour Space Attentional Deep Networks

Aims and Background: Image dehazing is an essential task in computer-vision, aimed at enhancing the clarity and visibility of images degraded by fog and haze. Traditional methods often struggle with handling the complex scattering effects of haze and maintaining the natural colours of the scene. Objectives and Methodology: To address these challenges, we propose a novel approach termed Dual Colour Space Attentional Deep Network (DCSADN) for efficient image dehazing. Our method leverages the advantages of two-colour spaces: RGB and YCbCr, to boost the network's skill to capture both the luminance and chrominance information, thereby improving the dehazing performance. We employ a multi-stage training strategy to optimize the performance of the DCSADN. This multi-stage approach ensures that the network generalizes well across different types of haze conditions. Extensive experiments demonstrate the superiority of our method over state-of-the-art dehazing techniques. Results: The results indicate that our approach not only achieves higher quantitative scores in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) but also produces dehazed images with more natural colours and details. Conclusion: The findings reveal that the dual colour space processing and attentional modules are crucial for the enhanced performance of our method and providing a valuable tool for improving image quality in hazy conditions.

Unsupervised co-generation of foreground–background segmentation from Text-to-Image synthesis

Text-to-Image (T2I) synthesis is a challenging task requiring modelling both textual and image domains and their relationship. The substantial improvement in image quality achieved by recent works has paved the way for numerous applications such as language-aided image editing, computer-aided design, text-based image retrieval, and training data augmentation. In this work, we ask a simple question: Along with realistic images, can we obtain any useful by-product (e.g. foreground/background or multi-class segmentation masks, detection labels) in an unsupervised way that will also benefit other computer vision tasks and applications?. In an attempt to answer this question, we explore generating realistic images and their corresponding foreground/background segmentation masks from the given text. To achieve this, we experiment the concept of co-segmentation along with GAN. Specifically, a novel GAN architecture called Co-Segmentation Inspired GAN (COS-GAN) is proposed that generates two or more images simultaneously from different noise vectors and utilises a spatial co-attention mechanism between the image features to produce realistic segmentation masks for each of the generated images. The advantages of such an architecture are two-fold: (1) The generated segmentation masks can be used to focus on foreground and background exclusively to improve the quality of generated images, and (2) the segmentation masks can be used as a training target for other tasks, such as object localisation and segmentation. Extensive experiments conducted on CUB, Oxford-102, and COCO datasets show that COS-GAN is able to improve visual quality and generate reliable foreground/background masks for the generated images.

A novel non-contrast agent-enhanced 3D whole-heart magnetic resonance sequence for congenital heart disease patients: the REACT Study.

The three-dimensional balanced-steady-state-free-precession (3D bSSFP) whole-heart (WH) technique has long been used to depict cardiac morphology in congenital heart disease (CHD) but is prone to banding artifacts. The Relaxation Enhanced Angiography without Contrast and Triggering (REACT) sequence is an alternative method that is resistant to off-resonance effects. To evaluate cardiac structures and great vessels in CHD patients using 3D WH REACT sequence and compare it to 3D WH bSSFP sequence. This study was approved by the Institutional Review Board. Thirty CHD patients were prospectively enrolled. Contrast-to-noise ratio (CNR), image quality, and cross-sectional area (CSA) were analyzed. Categorical data were compared with a Wilcoxon signed-rank test and normally distributed variables with a t-test. Thirty patients (16 females) participated in this study (median age 17, range 5months to 52years). REACT showed higher CNR in all pulmonary veins (all P<0.05), while 3D bSSFP had higher CNR in the right ventricle (P<0.001) and right pulmonary artery, (P=0.04). Image quality favored 3D bSSFP in the right atrium and ventricle (both P<0.001), main pulmonary artery (P=0.02), and coronary arteries (left: P<0.001, right: P=0.01). REACT outperformed 3D bSSFP for the pulmonary veins (all P<0.05) from image quality perspective. CSA measurements were not significantly different between REACT and 3D bSSFP (all P≥0.05). The REACT method is associated with improved image quality and CNR for pulmonary veins, with CSA measurements concordant with 3D bSSFP in CHD patients, while bSSFP shows better performance for imaging cardiac chambers and coronary arteries.

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Improving Image Quality and Visualization of Hepatocellular Carcinoma in Arterial Phase Imaging Using Contrast Enhancement-Boost Technique.

This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique. This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures. The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images (P < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images (P < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images (P < 0.001). The overall image quality and visibility of HCC were improved using the CE-boost technique.

Crop Leaf Type Classification and Multi-Class Leaf Disease Identification with Tangent Hunter Prey Optimization-Based LeNet

The early detection and accurate rate of classification of plant leaf disease are more important for reliable and good agriculture, which prevents unwanted financial loss and resources. In this study, Tangent Hunter Prey Optimization-based LeNet (THPO-LeNet) is utilized for crop leaf classification and multi-class plant leaf disease identification. In this case, the input image that goes through the image pre-processing step is obtained from the plant village dataset. An adaptive Wiener filter is applied at the pre-processing stage to reduce needless mistakes and improve image quality. The pre-processed image is then sent to the leaf segmentation step, where a Mask Region-based Convolution Neural Network (Mask R-CNN) performs the segmentation. Consequently, image augmentation is performed using techniques such as scaling, rotation, translation, flipping, contrast, saturation, and hue. Afterwards, first-level classification plant leaf classification is processed by LeNet, which is optimized by Tangent Hunter Prey Optimization (THPO). The THPO is the incorporation of a Tangent Search Algorithm (TSA) and Hunter–Prey Optimizer (HPO). At last, the second-level classification of plant leaf disease is conducted by THPO-LeNet. Furthermore, the efficiency of THPO-LeNet is examined based on measures, like accuracy, Negative Predictive Value (NPV), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), loss value, and False Positive Rate (FPR), and the value attained is 95.68%, 92.50%, 91.48%, 92.25%, 92.54%, 8.321%, and 7.754%, respectively.

Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.