Clear Images Research Articles

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

Gaussian second derivative blur kernels for image deblurring

Image blur is a common image degradation phenomenon that occurs in case of camera movement and lens defocusing. In order to deblur the image, point spread function (PSF) model is often used to match the real blur kernel, but currently the matching effect is poor. In this paper, an image restoration technique based on the Gaussian Second Derivative (GSD) fitted PSF is proposed to address this problem. Firstly, a GSD model is proposed. The actual image Line spread function (LSF) extracted by knife-edge method is modeled as a linear combination of a series of Gaussian second derivatives. Then, an improved annihilation filtering algorithm is proposed. The algorithm is used to estimate the parameters of the GSD model, so as to remove the errors in the actual complex scenarios. Finally, the image PSF is obtained by using the LSF curve fitted and corrected by the GSD model, and combined with the existing Richardson-Lucy (R-L) algorithm, the clear image is finally restored. Experimental results show that compared with the existing image restoration strategies based on other PSF models, this method can effectively improve the image restoration accuracy.

An effective ultrasound fetal palate screening software based on the “sequential sector scan through the oral fissure” and three-dimensional ultrasound

BackgroundOrofacial clefts are one of the most common congenital malformations of the fetal face and ultrasound is mainly responsible for its diagnosis. It is difficult to view the fetal palate, so there is currently no unified standard for fetal palate screening, and the diagnosis of cleft palate is not included in the relevant prenatal ultrasound screening guidelines. Many prenatal diagnoses for cleft palate are missed due to the lack of effective screening methods. Therefore, it is imperative to increase the display rate of the fetal palate, which would improve the detection rate and diagnostic accuracy for cleft palate. We aim to introduce a fetal palate screening software based on the “sequential sector scan though the oral fissure”, an effective method for fetal palate screening which was verified by our follow up results and three-dimensional ultrasound and to evaluate its feasibility and clinical practicability.MethodsA software was designed and programmed based on “sequential sector scan through the oral fissure” and three-dimensional ultrasound. The three-dimensional ultrasound volume data of the fetal face were imported into the software. Then, the median sagittal plane was taken as the reference interface, the anterior upper margin of the mandibular alveolar bone was selected as the fulcrum, the interval angles, and the number of layers of the sector scan were set, after which the automatic scan was performed. Thus, the sector scan sequential planes of the mandibular alveolar bone, pharynx, soft palate, hard palate, and maxillary alveolar bone were obtained in sequence to display and evaluate the palate. In addition, the feasibility and accuracy of the software in fetal palate displaying and screening was evaluated by actual clinical cases.ResultsFull views of the normal fetal palates and the defective parts of the cleft palates were displayed, and relatively clear sequential tomographic images and continuous dynamic videos were formed after the three-dimensional volume data of 10 normal fetal palates and 10 cleft palates were imported into the software.ConclusionsThe software can display fetal palates more directly which might allow for a new method of fetal palate screening and cleft palate diagnosis.

(Invited) Designing Quantum Dots for Multiplexed Deep-Tissue Imaging in the NIR-II

The combination of bright, tunable semiconductor quantum dots (QDs) emitting in the shortwave infrared or second near infrared window (SWIR/NIR-II, 1000 – 1600 nm) and InGaAs camera-equipped preclinical imagers opens the door to non-invasive, multiplexed, deep tissue imaging in live mice. While imaging in the NIR-I is like imaging through fog due to highly scattering tissue, imaging in the SWIR/NIR-II is more like imaging through pond water, enabling deeper imaging with much improved resolution. We synthesized multiple PbS/CdS core/shell QDs with emission wavelengths spanning 1000-1600 nm. These particles have been encapsulated in lipid-PEG micelles and up to three colors (so far) dosed into nude mice for imaging on a Photon Etc IR VIVO imager equipped with a hyperspectral Bragg grating and 6 bandpass filters, enabling demixing of the discrete colors. We have obtained exceptionally clear images of lymphatic drainage and the vasculature system, including visualization of lymphatic valve opening in videos. Interestingly, following intravascular injection, the liver and spleen are very visible and distinct, enabling the non-invasive visualization of a key nanomedicine drug delivery parameter (i.e., are the nanoparticles accumulating in the liver or the spleen?). Image processing investigations are expanding our ability to demix colors as well as to determine the average depth of the contrast agent, based on the wavelength-dependent attenuation of the photoluminescence based on water absorption. In addition to the photophysical advantages of using QDs for NIR-II, elemental analysis of blood and tissue samples enables quantitative biodistribution and pharmacokinetic studies. Ongoing work involves the application of this imaging approach to assess the impact nanomaterial surface properties and route of delivery on their pharmacokinetics and the use of deep tissue paired agent imaging for non-invasive “optical biopsy” of orthotopic tumors. Current data are already visually stunning, and ongoing imaging highlights new opportunities for research with every experiment! Figure 1

Binaphthalene-Assisted Axial Chirality in Porphyrins: Toward Solid-State Circularly Polarized Luminescence from Self-Assembled Nanostructures.

Circularly polarized luminescence (CPL) is emerging as an effective tool to study the excited-state optical activity in molecules and their self-assembled nanostructures. Chiral porphyrins are a class of optically active molecules wherein the ground-state chirality has been extensively studied in recent times using circular dichroism (CD) spectroscopy. However, obtaining CPL from porphyrin nanostructures, which would have vast implications in biological applications, has remained an uphill task. In this work, we design and synthesize a pair of chiral porphyrin enantiomers functionalized by axially chiral binaphthalene units at the four meso-positions. The molecule undergoes self-assembly following an isodesmic polymerization model, leading to the formation of a spherical nanostructure possessing opposite chirality. Favorable thermodynamic parameters achieved through the controlled experimental conditions helped drive the self-assembly in the forward direction. The limitations imposed by a large nonradiative decay constant arising due to the aggregation-induced quenching could be overcome by fabricating self-standing polymeric films of the nanostructures. The films exhibited relatively high radiative decay and, more interestingly, good CPL activity with clear mirror image spectra for the nanostructures with opposite chirality. The work on CPL-active solid-state materials opens avenue for the design and synthesis of a variety of porphyrin-based chromophoric systems and their nanoaggregates that can find potential application in the field of chiral biosensing and bioimaging, security tags, and display devices.

The future of general practice and family medicine in Austria.

General practice/family medicine has recently been recognized as amedical discipline in Austria. This paper is ashort report on the prevailing understanding of its goals and subjects, comparing the Austrian perception with international definitions. It comments on shortcomings and introduces an outline for the development of arevised professional theory.At present, there is no clear uniform image of the discipline, neither among the general public, nor among physicians, healthcare professionals or decision makers. The reason for this lies in the historical development which, with the triumph of specialization, has led to aloss of importance for generalist medicine. Now it is the fragmentation that extensive specialization entails that gives anew meaning to generalist, contextual and patient-centered medicine.This change needs to be analyzed and understood. Adescription of the responsibilities, tasks and very specific methods unique to the discipline will be developed, which should enable the sensible, contemporary use of general practice/family medicine for the benefit of patients and the healthcare system.

Coarse-to-fine mechanisms mitigate diffusion limitations on image restoration

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) to mitigate diffusion limitations mentioned before on image restoration. Specifically, the proposed C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training mechanism. The DFSA and DFN with embedded diffusion steps respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to guide the restoration process in different time steps. In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training pipeline. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on 3 tasks, including image deraining, image deblurring, and real image denoising. The source codes and visual results are available at https://github.com/wlydlut/C2F-DFT.

A comprehensive review of binocular vision in myopia control

Myopia is a growing problem worldwide and is particularly common among young people. Myopia is a disease that affects the retina responsible for detail in the middle eye and can lead to permanent vision loss. Myopia, a prevalent refractive error causing distant objects to appear blurred, is increasingly affecting global populations, particularly children. By 2050, it's projected that half the world's population will be myopic, largely due to genetic predispositions and environmental factors like excessive near work and limited outdoor activities. A promising approach to managing myopia is binocular vision training, which involves using both eyes to create a single, clear image, potentially slowing myopia progression. Understanding the benefits of this method is critical as we move forward, especially considering the surge in myopia cases during the COVID-19 pandemic due to increased digital device use. Research underscores the importance of addressing lifestyle and promoting outdoor activities and mitigating the impact of near work can help manage myopia progression and associated binocular vision disorders. Binocular vision training shows promise for myopia control by enhancing eye coordination, potentially slowing progression. Key factors include accommodation, AC/A ratios, and phoria, which are essential for effective diagnostics and therapy. Recent research emphasizes a holistic assessment of these elements for precise management of myopia and related binocular vision disorders in children. Understanding the mechanisms and innovative approaches of binocular vision in controlling myopia, highlighting the significance of continuous research and patient education in improving eye health.

Understanding validity criteria in technology-enhanced learning: A systematic literature review

Technological aids are ubiquitous in today's educational environments. Whereas much of the dust has settled in the debate on how to validate traditional educational solutions, in the area of technology-enhanced learning (TEL) many questions still remain. Technologies often abstract away student behaviour by condensing actions into numbers, meaning teachers have to assess student data rather than observing students directly. With the rapid adoption of artificial intelligence in education, it is timely to obtain a clear image of the landscape of validity criteria relevant to TEL. In this paper, we conduct a systematic review of research on TEL interventions, where we combine active learning for title and abstract screening with a backward snowballing phase. We extract information on the validity criteria used to evaluate TEL solutions, along with the methods employed to measure these criteria. By combining data on the research methods (qualitative versus quantitative) and knowledge source (theory versus practice) used to inform validity criteria, we ground our results epistemologically. We find that validity criteria tend to be assessed more positively when quantitative methods are used and that validation framework usage is both rare and fragmented. Yet, we also find that the prevalence of different validity criteria and the research methods used to assess them are relatively stable over time, implying that a strong foundation exists to design holistic validation frameworks with the potential to become commonplace in TEL research.

Tailoring emission characteristics of Na+-Sc3+ co-substituted CaMgGe2O6:Cr3+ phosphors for iris acquisition

The growing interest in Cr3+-doped broadband infrared phosphors stems from their promising applications in various fields. However, achieving highly efficient broadband emission based on these materials remains a significant challenge. To address this issue, we proposed a cation co-substitution method to enhance the near-infrared (NIR) emission of Cr3+-doped phosphors while simultaneously broadening the full width at half maximum (FWHM) of their emission peaks, as demonstrated in the case of CaMgGe2O6:Cr3+. Through controlled co-substitution of Ca2+ and Mg2+ cations by Na+ and Sc3+ cations, we prepared (Ca, Mg)1-x(Na, Sc)xGe2O6:Cr3+ (CMSxG:Cr3+) using a solid reaction method. This approach aims to deliberately adjust the strength of the crystal field experienced by Cr3+ to regulate the luminescence performance of the corresponding phosphors. Indeed, we observed a red shift of the emission peak from 850 to 902 nm, with the FWHM extending from 163 to 198 nm, as the concentration of ‘Na+-Sc3+’ pair increased, which would benefit iris acquisition. Importantly, the external quantum yield did not deteriorate; instead, it was enhanced from 13.6 to 26.2 %. Finally, we fabricated a broadband near infrared phosphor converted light-emitting diodes (pc-LED) by integrating CMSxG:Cr3+ phosphors with commercial 460 nm LEDs. An infrared output power of 118.01 mW at 300.00 mA was achieved with pc-LED based on Ca0.9Mg0.88(NaSc)0.1Ge2O6:0.02Cr3+ phosphor. Clear iris images acquired using these pc-LED as lighting sources demonstrate their potential applications in iris acquisition for biometrics.

Photoacoustic imaging for three-dimensional bioprinted constructs

Bioimaging is used to inspect the successful growth and functional differentiation of cells in printed biomaterials, which are ultimately finalized into functional artificial tissues capable of replacing native tissues. While optical bioimaging techniques are commonly utilized, the current trend in three-dimensional (3D) bioprinting towards replicating complex 3D microarchitectures poses a challenge for conventional optical imaging techniques in providing clear cross-sectional images due to the opaque nature of tissue. Consequently, these limitations necessitate lengthy and destructive preparation processes, which are associated with sacrificing cell viability and damaging the bioprinted material. Photoacoustic imaging (PAI) is a versatile imaging technique that extends the advantages of the optical bioimaging technique to undiscovered depths enabled by its acoustic hybridity, making itself a promising tool for non-destructive imaging of 3D bioprinted constructs. In this review, we introduce the flexible spectral contrasts provided by PAI, which are potentially applicable to 3D-bioprinted constructs, and summarize bioprinting studies that functionally implement PAI for in vitro and in vivo assessments. Finally, we provide an outlook on practical considerations for the more complete integration of these two fields, anticipating more fruitful discoveries as bioprinting advances towards more complex hierarchies.

The Development of Spinal Endoscopic Ultrasonic Imaging System with an Automated Tissue Recognition Algorithm.

Preclinical experimental study. To develop an intraoperative ultrasound-assisted imaging device, which could be placed at the surgical site through an endoscopic working channel and which could help surgeon recognition of different tissue types during endoscopic spinal surgery (ESS). ESS remains a challenging task for spinal surgeons. Great proficiency and experience are needed to perform procedures such as intervertebral discectomy and neural decompression within a narrow channel. The limited surgical view poses a risk of damaging important structures, such as nerve roots. We constructed a spinal endoscopic ultrasound system, using a 4-mm custom ultrasound probe, which can be easily inserted through the ESS working channel, allowing up to 10 mm depth detection. This system was applied to ovine lumbar spine samples to obtain ultrasound images. Subsequently, we proposed a two-stage classification algorithm, based on a pretrained DenseNet architecture for automated tissue recognition. The recognition algorithm was evaluated using accuracy and consistency. The probe can be easily used in the ESS working channel and produce clear and characteristic ultrasound images. We collected 367 images for training and testing of the recognition algorithm, including images of the spinal cord, nucleus pulposus, adipose tissue, bone, annulus fibrosus and nerve roots. The algorithm achieved over 90% accuracy in recognizing all types of tissues with a Kappa value of 0.875. The recognition times were under 0.1 s using the current configuration. Our system was able to be used in existing ESS working channels and clearly identified at-risk spinal structures in vitro. The pretrained algorithms could identify six intraspinal tissue types accurately and quickly. The concept and innovative application of intraoperative ultrasound in ESS may shorten the learning curve of ESS and improve surgical efficiency and safety.

Near-infrared optical properties of Cs2KScF6:Cr3+ phosphor for high-power light-emitting diodes

The recent years have witnessed a great leap for near-infrared (NIR) spectroscopy technology in multifunctional application fields. In this study, a double-perovskite structured Cr3+-doped Cs2KScF6 fluoride NIR phosphor with peak location at 778 nm and full-width at half maximum (FWHM) of 95 nm was synthesized by a co-precipitation method. The integral emission intensity at 423 K retains 70 % of that at room temperature (RT), showing good luminescent thermal stability. Combining the obtained phosphor in an NIR phosphor converted light-emitting diode (NIR pc-LED) device, the output power and photoelectric conversion efficiency can reach 72.28 mW and 6.83 % at 320 mA. By virtue of the good optical performance, clear structural images of different fruits were visualized by using the NIR pc-LED as light source, suggesting the potential of Cs2KScF6:Cr3+ phosphor to be used in high-power LED devices.

Neural Schrödinger bridge for unpaired real-world image deraining

Given the significant differences between domains, current unpaired learning methods struggle to accurately map the relationship between rainy and clear images. To this end, we introduce a neural Schrödinger bridge (NSB) for unpaired real-world image deraining, which utilizes the stochastic differential equations to capture the mapping relationships between rainy and clear domains. Meanwhile, we frame the deraining process as a Lagrangian problem using the Kullback-Leibler divergence between the data distribution and the model distribution. Additionally, by leveraging the capabilities of the contrastive language-image pre-training model (CLIP), our research shows that the CLIP prior helps differentiate between rainy and clear images. Building on this, we reformulate the Schrödinger bridge problem as a series of adversarial learning tasks using both image and prompt representations. To our knowledge, our approach is the first to use the Schrödinger bridge in unpaired image deraining. Extensive experiments show that our proposed NSB model outperforms existing unpaired deraining methods in both quantitative and qualitative evaluations.

Unsupervised multi-branch network with high-frequency enhancement for image dehazing

Recently, CycleGAN-based methods have been widely applied to the unsupervised image dehazing and achieved significant results. However, most existing CycleGAN-based methods ignore that the input of the generator contains two different distributions of data which can often lead to confusion in the learning process of the generator, consequently limiting the final dehazing performance. Moreover, reconstructing clear images through model architecture design and loss functions is an indirect constraint, making it difficult to compensate for the missing high-frequency information, such as textures and structures in the extracted features from hazy images. To address these issues, in this paper, we propose an Unsupervised Multi-Branch with High-Frequency Enhancement Network (UME-Net) which contain an Multi-Branch Dehazing Network (MBDN) and a High-Frequency Components Enhancement Module (HFEM). Specifically, MBDN constructs a single unsupervised dehazing network with Shared Encoding Module (SEM) and Multi-Branch Decoding Module (MDM). SEM enhance the consistency of feature representation and MDM effectively addresses the confusion during the generator learning process in CycleGAN-based methods. Furthermore, based on a key observation that hazy images and their corresponding clear images exhibit only subtle differences in high-frequency information, the HFEM is designed to compensates for the missing high-frequency information in the network which further enhances the reconstruction capability of the UME-Net for restore edge and texture information in obscured by dense haze. Experimental results on challenging benchmark datasets demonstrate the superiority of our UME-Net over SOTA unsupervised image dehazing methods. The source code is available at https://www.github.com/thislzm/UME-Net.

Engineering for a clear image: a comparative focus on accommodation.

The eye requires the ability to focus images near and far and throughout evolution numerous mechanisms have developed to allow this accommodation. From primitive organisms which use a small pupil to effect pinhole camera optics without a lens through more complex eyes with a lens that is moved antero-posteriorly along the visual axis or the shape of which is changed, the eye has engineered numerous accommodative mechanisms. Human inventors have developed cameras with remarkable accommodative abilities but none match the remarkable focussing abilities of the four-eyed fish Anableps or the cormorant which similarly manages to focus above and below water, to give just two examples from the animal kingdom, perfectly adapted to their environments and behaviours.

Vision-Based Online Molten Pool Image Acquisition and Assessment for Quality Monitoring in Gas–Metal Arc Welding

In gas–metal arc welding (GMAW), the welding quality is influenced by various factors, including the welding conditions and external environment. Achieving a high welding quality requires careful observation and control. Welding monitoring is crucial for validating a planned welding process and assessing its quality. This study introduces a vision system for the online acquisition of clear molten pool images. It uses a small camera equipped with a complementary metal-oxide-semiconductor sensor during GMAW, as well as a method for constructing this system. The proposed vision system does not require triggering using devices such as field-programmable gate arrays, facilitating lower costs and online quality monitoring under various welding conditions during mild-steel GMAW. Accurate measurement of radiation from the weld is essential for clear observation of the molten pool during welding, emphasizing the importance of selecting an optical system with appropriate lenses and filters. In addition, appropriate digital camera parameters must be set for monitoring. High-quality images were successfully captured using the proposed method, and the image quality was evaluated using the blind/referenceless image spatial quality evaluator algorithm.

Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection.

Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble.

Neural Representations of Sensory Uncertainty and Confidence Are Associated with Perceptual Curiosity.

Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty), but the neural mechanisms of this link, have been little investigated. Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are read out by higher-order structures to generate signals of confidence and, ultimately, curiosity. We scanned participants (17 female, 15 male) using fMRI while they performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. We measured the activity evoked by each image in the occipitotemporal cortex (OTC) and devised a new metric of "OTC Certainty" indicating the strength of evidence this activity conveys about the animal versus object categories. We show that, perceptual curiosity peaked at low confidence and OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Moreover, univariate (average) activity in two frontal areas-vmPFC and ACC-correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results reveal novel mechanisms through which uncertainty about an event generates curiosity about that event.

FastFaceCLIP: A lightweight text‐driven high‐quality face image manipulation

AbstractAlthough many new methods have emerged in text‐driven images, the large computational power required for model training causes these methods to have a slow training process. Additionally, these methods consume a considerable amount of video random access memory (VRAM) resources during training. When generating high‐resolution images, the VRAM resources are often insufficient, which results in the inability to generate high‐resolution images. Nevertheless, recent Vision Transformers (ViTs) advancements have demonstrated their image classification and recognition capabilities. Unlike the traditional Convolutional Neural Networks based methods, ViTs have a Transformer‐based architecture, leverage attention mechanisms to capture comprehensive global information, moreover enabling enhanced global understanding of images through inherent long‐range dependencies, thus extracting more robust features and achieving comparable results with reduced computational load. The adaptability of ViTs to text‐driven image manipulation was investigated. Specifically, existing image generation methods were refined and the FastFaceCLIP method was proposed by combining the image‐text semantic alignment function of the pre‐trained CLIP model with the high‐resolution image generation function of the proposed FastFace. Additionally, the Multi‐Axis Nested Transformer module was incorporated for advanced feature extraction from the latent space, generating higher‐resolution images that are further enhanced using the Real‐ESRGAN algorithm. Eventually, extensive face manipulation‐related tests on the CelebA‐HQ dataset challenge the proposed method and other related schemes, demonstrating that FastFaceCLIP effectively generates semantically accurate, visually realistic, and clear images using fewer parameters and less time.