Pixel Intensity Research Articles

Quantum-CMOS image sensor assessment for Cherenkov imaging during radiotherapy

Cherenkov emission from human tissues during linac-delivered radiotherapy provides a detectable optical signal that shows the real-time dose delivery. The challenge of this modality is that the linac pulses producing Cherenkov light have short durations (2–4 μs) with low duty cycle (0.1%), and the signal reaches the camera with near single photon per pixel intensity. In this study, the ability to use an ultra-low noise (<1 e−/pixel) quantum-CMOS sensor (qCMOS) to detect Cherenkov light was tested, with optimization of the signal integration and readout timings, as related to the linac pulse structure. The signal-to-noise ratio (SNR) of the detected Cherenkov was found to be sufficient for good image quality with SNR values ranging from 100 up to 400, depending on the exposure time set per frame with noise performance surpassing an intensifier-coupled CMOS camera (iCMOS) beyond ∼50 ms of exposure time per frame. The detected signal also correlated very closely to a planned dose in a water tank with gamma pass rates of >94%. Additional advantages include low susceptibility to stray x-ray radiation. This recent development of ultra-low noise qCMOS technology is a unique option for Cherenkov imaging, showing the ability to image signals well above the noise without need for image intensifiers or gain amplification.

Innovative colorimetric thermal study of methylcellulose hydrogel via smartphone imaging

A novel colorimetric analysis of methylcellulose (MC) hydrogel was conducted using a standard smartphone camera to measure its thermo-optical properties. As demonstrated for the first time, the temperature of MC gels was directly determined from photographs by exploiting a unique one-to-one correlation between temperature and mean pixel intensity in the blue channel of RGB images, all color channels showed strong hysteresis. Two innovative procedures for gelation assessment are introduced: variance analysis and histogram analysis with normal distribution fitting. The variance analysis confirms known gelation related temperatures, validating the effectiveness of the new method. Histogram analysis reveals a significant increase in RMSE (root mean square error) near gelation related points, offering a new indicator for gelation status. This methodology underscores the untapped potential of colorimetry to extract valuable data from hydrogels in general and MC gel in particular.

Lateral flow immunoassay for amatoxins detection in human urine compared to liquid chromatography-high-resolution tandem mass spectrometry.

Amatoxin-containing mushrooms, which contribute to many intoxications each year are of particular interest for clinicians and toxicologists as patients require special treatment in hospital. To confirm the presence of amatoxins, approaches for their fast, sensitive, and reliable identification must be available. Solid-phase extraction followed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) is widely applied for analysis of amatoxins as this combination provides suitable sensitivity, specificity, and mass accuracy. Nevertheless, time-consuming preparatory steps as well as expensive equipment is required. Therefore, a lateral flow immunoassay (LFIA) for trace detection of α-, β-, and γ-amanitin was established and evaluated using dog urine. In this study, we answered the questions whether this LFIA can be transferred to human urine samples, and whether this LFIA can be used as a supporting tool prior to LC-HRMS/MS confirmation. Result interpretation by eye and using digitally-acquired pixel intensity ratios was investigated with respect to analytical sensitivity. The LFIA detects amatoxins in human urine after visual evaluation to as little as 5 ng/mL (α-amanitin - 10 ng/mL, β-amanitin - 50 ng/mL, γ-amanitin - 5 ng/mL). After digital analysis, pixel intensity ratios were determined to evaluate the LFIA as positive, negative, or trace result. Detection limits were redefined ranging from 1 ng/mL (α- and γ-amanitin) to 3 ng/mL (β-amanitin). For the proof-of-concept, 73 human urine samples submitted to the authors´ laboratory for toxicological analysis were analyzed using the LFIA and LC-HRMS/MS. Only three out of 73 urine samples were tested false positive with the LFIA as LC-HRMS/MS confirmation revealed no detection of amatoxins. Sixteen urine samples were evaluated as trace results and confirmed negative using LC-HRMS/MS except for one case which was positive for α-amanitin but negative for β-amanitin. Although particularly positive and trace results of the LFIA still need to be confirmed, the negative LFIA results correlated well with LC-HRMS/MS.

Brain multi modality image inpainting via deep learning based edge region generative adversarial network.

A brain tumor (BT) is considered one of the most crucial and deadly diseases in the world, as it affects the central nervous system and its main functions. Headaches, nausea, and balance problems are caused by tumors pressing on nearby brain tissue and affecting its function. The existing techniques are challenging to analyze diseased brain images since abnormal brain tissues lead to distorted or biased results during image processing, like tissue segmentation and non-rigid registration. To overcome these issues, proposed a DS-GAN model for inpainting brain MRI images. Initially, the input MRI images are segmented using a Gated shape convolution neural network (GS-CNN). In the first GAN, grayscale pixel intensities and the remaining image edges are utilized to create edge generators or edge reconstruction Generative Adversarial Networks (EGAN), which are capable of creating false edges in areas that are missing. The results of the experimental results demonstrated that the Jaccard Index (JI) was 0.82, while the Dice Index (DI) was 0.86. The proposed DS-GAN in terms of L1 loss, PSNR, SSIM, and MSE obtained was 2.18, 0.972, 32.04, and 26.42. As compared to existing techniques, the proposed DS-GAN model achieves an overall accuracy of 99.18%.

Radiomic Feature Extraction from OCT Angiography of Idiopathic Epiretinal Membranes and Correlation with Visual Acuity: A Pilot Study.

To explore the correlation between radiomics features extracted from OCT angiography (OCTA) of epiretinal membranes (ERMs) and baseline best-corrected visual acuity (BCVA). Retrospective observational monocentric study. Eighty-three eyes affected by idiopathic ERMs, categorized into low (≤70 letters) and high (70 letters) BCVA groups. The central 3 × 3 mm2 crop of structural and vascular en-face OCTA scans of superficial and deep retina slab, and choriocapillaris of each eye was selected. PyRadiomics was used to extract 86 features belonging to 2 different families: intensity-based statistical features describing the gray-level distribution, and textural features capturing the spatial arrangement of pixels. By employing a greedy strategy, 4 radiomic features were selected to build the final logistic regression model. The ability of the model to discriminate between low and high baseline BCVA was quantified in terms of area under the receiver operating characteristics curve (AUC). The 4 selected informative radiomic features were as follows: the difference average (glcm_DifferenceAverage), quantifying the average difference in gray-level between neighboring pixels; the informational measure of correlation (glcm_Imc1), giving information about the spatial correlation of pixel intensities inside the image; the long run low gray-level emphasis (glrlm_LongRunLowGrayLevelEmphasis), highlighting long segments of low gray-level values within the image; and the large area emphasis (glszm_LargeAreaEmphasis), which quantifies the tendency for larger zones of uniform intensity to occur. No features exhibited a statistically significant difference between low and high BCVA values for the superficial and deep retinal slabs. Conversely, in the choriocapillaris layer, the glcm_DifferenceAverage and glcm_Imc1 features were significantly higher in the high BCVA group (P = 0.047), whereas higher values for the glrlm_LongRunLowGrayLevelEmphasis and glszm_LargeAreaEmphasis were associated with the low BCVA group (P = 0.047). Overall, these radiomic features predicted BCVA with an AUC (95% confidence interval) of 0.74 (0.63-0.85) and sensitivity/specificity of 0.67/0.75. During the cross-validation, the metrics remained stable. Radiomics features of the choriocapillaris in idiopathic ERMs showed a correlation with BCVA, with lower structural complexity and higher homogeneity, together with the presence of homogeneous areas with low-intensity pixel values, reflecting flow voids due to reduced microvascular perfusion, and were correlated with lower visual acuity. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

High-dynamic-range 3D shape measurement: the original and inverted fringe projection method combined with the K-means clustering exposure time estimation algorithm

It is a challenge for fringe projection profilometry to reconstruct the three-dimensional (3D) shape of highly reflective objects due to the saturation of the captured images. In the multiple exposure method (MEM), exposure time is not predetermined, which requires numerous adjustments to achieve a wide range of intensity fringe sequences. This results in low measurement efficiency. To address this, this paper proposes an original and inverted fringe projection method (OIFPM) combined with a K-means clustering exposure time estimation algorithm. First, we apply the K-means clustering algorithm to classify the intensity levels of each pixel in the captured image, which allows for the estimation of the optimal exposure time. Subsequently, we combine the above method with OIFPM to capture original and inverted fringe images at each estimated exposure time and select the maximum intensity but unsaturated pixels to synthesize the final fringe image. Finally, the phase calculation is performed using 72 wrapped phase equations, and these equations are presented in detail. Experimental results demonstrate that the proposed approach performs excellently in the 3D measurement of highly reflective objects. It significantly outperforms both OIFPM and the random triple exposure time method, achieving results comparable to those of MEM while requiring fewer fringe images. This makes the proposed method particularly suitable for the 3D measurement of highly reflective objects.

Chemical profiling of testicular parenchyma in rams using an exploration of echointensity bands and the novel r-Algo computer algorithm increasing precision and accuracy of ultrasound image analyses.

Chemical profiling of testicular parenchyma in rams using an exploration of echointensity bands and the novel r-Algo computer algorithm increasing precision and accuracy of ultrasound image analyses.

Viability of video imaging spectro-radiometry (VISR) for quantifying flare combustion efficiency

ABSTRACT Video imaging spectro-radiometry (VISR) has been proposed as a means to quantify the combustion efficiency (CE) of flares. This work presents a numerical assessment of VISR using computational fluid dynamics simulations of a steam-assisted industrial flare, with a focus on three aspects: how approximations in the spectroscopic model impact the local “pixel-wise” CE, the validity of the approach for computing flare global CE using inferred local CE values, and the ability and limitations of VISR instrument to capture fuel that may be aerodynamically stripped from the combustion zone under crosswind conditions. The present analysis is conducted using simulated images generated over bands aligned with absorption features of three key products of flare combustion: CO2 (4.2–4.4 µm), CO (4.5–4.9 µm), and CH4 (3.2–3.4 µm). The results show that the simplified VISR approach can predict local CE accurately, but the model used to convert these values into a flare global CE is flawed and potentially leads to large biases. Finally, since the technique relies on mid-infrared imaging, it is likely incapable of quantifying unburned (cold) methane that may be stripped from the combustion zone due to the presence of a high crosswind over the flare stack, leading to a significant overestimation of the actual flare performance. Implications Statement A technique called “Video imaging spectro-radiometry” (VISR) has been developed for quantifying the combustion efficiency of flares based on spectrally resolved imaging. In the original version of this technique, a multispectral camera measures emissions over spectral bands aligned with key absorption features of CO2, CO, and the C-H stretch absorption band of alkanes. A local combustion efficiency map is defined from the ratio of the broadband pixel intensities, which is then converted into an overall combustion efficiency through pixel-averaging. While this technique has been validated through extractive sampling studies, in this work we analyze simulated measurements using a CFD simulated steam-assisted flare. In this context the CFD data serves as a ground truth. The results call into question the veracity of the instrument model used to convert the local CE estimates into a global CE for the flare, as well as the ability of this technique to capture cold methane that may be diverted from the combustion zone through aerodynamic stripping. These findings have important implications for emerging technology-based emission regulations, as well as the development of new remote sensing technologies for measuring flare performance.

Quantitative in Vivo Assessment of Intraocular Lens Calcification: Correlation Between Optical Coherence Tomography Opacity and Straylight Measurement: In Vivo Assessment of IOL Opacification using OCT.

Quantitative in Vivo Assessment of Intraocular Lens Calcification: Correlation Between Optical Coherence Tomography Opacity and Straylight Measurement: In Vivo Assessment of IOL Opacification using OCT.

A Novel Ensemble Empirical Decomposition and Time–Frequency Analysis Approach for Vibroarthrographic Signal Processing

Abstract Signal processing techniques play a critical role in addressing real-world applications across domains such as sensor analysis, defence, and clinical and biomedical fields. Within healthcare, computer-aided diagnostic (CAD) systems have become pivotal in supporting medical professionals with the interpretation of data and images, especially in medical imaging and radiological diagnostics. For diagnosing joint disorders, both time-domain and frequency-domain analyses are employed to examine complex, non-stationary, and nonlinear signals. To process Vibroarthrographic signals in this context, an initial step involves applying the Hilbert-Huang Transform, which comprises two stages: Empirical Mode Decomposition (EMD) for computing intrinsic mode functions (IMFs), followed by the Hilbert transform for further signal analysis. In our proposed approach, we utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Time-Varying Frequency Empirical Mode Decomposition (TVF-EMD) to compute IMFs, as well as Variation Mode Decomposition to calculate mode signals. Subsequent feature extraction incorporates both time and frequency characteristics, focusing on metrics such as pixel intensity, mean, and standard deviation. These features then serve as inputs to machine learning models for classification tasks, distinguishing between healthy and non-healthy signal samples. In our model, we employed a Least Squares Support Vector Machine (LS-SVM) and a Support Vector Machine with Recursive Feature Elimination (SVM-RFE) to enhance classification accuracy. This sequence of signal processing and machine learning steps demonstrates a structured and effective approach for CAD-based diagnosis in joint disorder assessments.

NightHaze: Nighttime Image Dehazing via Self-Prior Learning

Masked autoencoder (MAE) shows that severe augmentation during training produces robust representations for high-level tasks. This paper brings the MAE-like framework to nighttime image enhancement, demonstrating that severe augmentation during training produces strong network priors that are resilient to real-world night haze degradations. We propose a novel nighttime image dehazing method with self-prior learning. Our main novelty lies in the design of severe augmentation, which allows our model to learn robust priors. Unlike MAE that uses masking, we leverage two key challenging factors of nighttime images as augmentation: light effects and noise. During training, we intentionally degrade clear images by blending them with light effects as well as by adding noise, and subsequently restore the clear images. This enables our model to learn clear background priors. By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors. While our self-prior learning is considerably effective in suppressing glow and revealing details of background scenes, in some cases, there are still some undesired artifacts that remain, particularly in the forms of over-suppression. To address these artifacts, we propose a self-refinement module based on the semi-supervised teacher-student framework. Our NightHaze, especially our MAE-like self-prior learning, shows that models trained with severe augmentation effectively improve the visibility of input haze images, approaching the clarity of clear nighttime images. Extensive experiments demonstrate that our NightHaze achieves state-of-the-art performance, outperforming existing nighttime image dehazing methods by a substantial margin of 15.5% for MUSIQ and 23.5% for ClipIQA.

Moving target segmentation method based on tensor decomposition and graph Laplacian regularization

A new method for segmenting the target foreground from image frames is proposed by utilizing the theory of graph signal processing and the tensor decomposition model aiming at the problem that the segmentation results of the existing foreground segmentation methods in image frames under dynamic scenes are not high in accuracy. The intrinsic connection between image pixels in each frame of an image sequence is modeled as a graph, the image pixel intensities are modeled as graph signals, and the correlation between pixels is characterized by the graph model. According to the significant difference between the dynamic background and the target change in the moving foreground in the image sequence, the dynamic background region in each image frame is smoothed and suppressed, and the disturbing information of the dynamic background is transformed into the useful component information in the low-rank subspace. The connectivity between image pixels can be characterized by the graph Laplacian regularization term, and then the target foreground segmentation problem in the image sequence is equivalent to a constrained optimization problem with tensor decomposition and graph Laplacian regularization term. The alternating direction multiplier method is used to solve the optimization problem, and the simulation results on real scene data set verify the effectiveness of the algorithm.

Enhancing Image Security with Memristor Driven Fractional Chaotic Systems and Secretary Bird Optimization

The extensive utilization of information and communication technologies nowadays enhances information accessibility and underscores the importance of information and data security. Image encryption is a prevalent technique for safeguarding medical data on public networks, serving a vital function in the healthcare sector. Due to their intricate dynamics, memristors are frequently employed in the creation of innovative chaotic systems that enhance the efficacy of chaos-based encryption techniques. In recent years, chaos-based encryption methods have surfaced as a viable method for safeguarding the confidentiality of transmitted images. Memristor-based Fractional-order chaotic systems (MFOCS) have garnered considerable interest because to their resilience and intricacy. Fractional-order chaotic systems (FOCS) exhibit more intricate dynamics than integer-order chaotic systems. Consequently, the exploration of fractional chaotic systems for the development of picture cryptosystems has gained popularity recently. This research introduces an innovative image encryption framework utilizing a memristor-based fractional chaotic map in conjunction with the Secretary Bird Optimization Algorithm (SBOA) to improve security and resilience against cryptographic threats. The suggested method utilizes the distinctive memory properties and high-dimensional chaotic dynamics of the memristor-based fractional system to produce unpredictable encryption keys. The SBOA is utilized to enhance essential encryption parameters, guaranteeing superior randomness and resilience against statistical and differential assaults. The encryption method comprises a confusion phase, in which pixel positions are randomized using chaotic sequences, succeeded by a diffusion phase, where pixel intensities are altered utilizing optimal key sequences. Performance evaluation is executed by entropy analysis, correlation coefficient tests, NPCR, UACI, and studies of computational complexity. The findings indicate that the suggested method attains elevated entropy, minimal correlation, and robust key sensitivity, rendering it exceptionally resilient against brute-force and differential assaults. Notwithstanding its computing burden from fractional-order chaotic dynamics, the suggested model offers a secure and efficient encryption method appropriate for real-time image protection applications.

Innovative fusion: MRSI-guided brain tumour classification via integrated image segmentation and GLCM feature extraction

ABSTRACT Accurate brain tumour classification is crucial for effective diagnosis and treatment planning. This study aims to develop an advanced classification approach by integrating Magnetic Resonance Spectroscopy Imaging (MRSI) segmentation with Grey Level Co Occurrence Matrix (GLCM) feature extraction to improve glioma classification. Using MR images from 52 subjects, we examined various glioma metabolites and computed metabolite ratios within segmented tumour regions. GLCM features provided insights into pixel intensity spatial distributions and metabolic alterations, aiding in differentiation of tumour types and grades. Our method, employing image preprocessing, Otsu’s thresholding for segmentation, and CNN Efficient Net for classification, achieved nearly 90% accuracy in high-grade glioma identification. The confusion matrix demonstrated strong classification performance, with a precision of 0.89 for healthy subjects and 0.92 for glioma cases, while recall values were 0.92 and 0.88, respectively. The F1-scores of 0.91 (healthy) and 0.90 (glioma) further indicate the model’s balanced predictive capability. Additionally, the Receiver Operating Characteristic (ROC) curve yielded an AUC of 0.95, emphasising the effectiveness of the proposed approach. Significant correlations were found between model efficacy, metabolite ratios, and GLCM features, highlighting the efficiency of our method in brain tumour classification and its potential clinical relevance.

Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.

Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition accuracy. Nevertheless, these DNNs are inherently characterized by static, continuous-valued neuron activations, necessitating intricate network architectures and extensive parameter training to enhance performance. To address these challenges, we introduce an adaptive firing threshold-based spiking neural network (ATSNN) for FVR. ATSNN leverages discrete spike encodings to transforms static finger vein images into spike trains with spatio-temporal dynamic features. Initially, Gabor and difference of Gaussian (DoG) filters are employed to convert image pixel intensities into spike latency encodings. Subsequently, these spike encodings are fed into the ATSNN, where spiking features are extracted using biologically plausible local learning rules. Our proposed ATSNN dynamically adjusts the firing thresholds of neurons based on average potential tensors, thereby enabling adaptive modulation of the neuronal input-output response and enhancing network robustness. Ultimately, the spiking features with the earliest emission times are retained and utilized for classifier training via a support vector machine (SVM). Extensive experiments conducted across three benchmark finger vein datasets reveal that our ATSNN model not only achieves remarkable recognition accuracy but also excels in terms of reduced parameter count and model complexity, surpassing several existing FVR methods. Furthermore, the sparse and event-driven nature of our ATSNN renders it more biologically plausible compared to traditional DNNs.

A reliable experimental protocol to quantify the mixing of liquids in microchannels

Mixing of liquids in a microchannel is critical for many applications of microfluidics. However, experimental quantification of mixing is a challenge, and a number of methods have been proposed. A typical approach to quantifying mixing involves measuring the mixing index (MI), which is a function of the standard deviation of pixel intensity distribution across the channel cross section. In calculating MI, usually, the measured pixel intensities are stretched/normalized and rescaled such that the smallest and largest intensities are assigned as 0 and 1. Consequently, the fully mixed and unmixed states are identified and assigned the average intensity (⟨I⟩) of 0.5. This artificial scaling makes comparison of MI found across studies with varied geometry and mixing mechanisms difficult. In this study, it is shown that precise measurement of ⟨I⟩ is crucial for determining the MI. A new experimental protocol to measure MI is proposed, and the efficacy of this approach is demonstrated by determining the MI for two passive configurations; in one, the mixing is purely diffusive, and in another, both diffusive and advective mixing occurs. It is found that MI values from experiments and numerical simulations agree to within 5% when both advective and diffusive mixing occurs. By incorporating the data from this study in prominent formulas in the literature for calculating MI, it is illustrated that our experimentally determined MI values have the closest agreement with results from simulation.

A Benchmark Dataset for the Validation of Phase-Based Motion Magnification-Based Experimental Modal Analysis

In recent years, the development of computer vision technology has led to significant implementations of non-contact structural identification. This study investigates the performance offered by the Phase-Based Motion Magnification (PBMM) algorithm, which employs video acquisitions to estimate the displacements of target pixels and amplify vibrations occurring within a desired frequency band. Using low-cost acquisition setups, this technique can potentially replace the pointwise measurements provided by traditional contact sensors. The main novelty of this experimental research is the validation of PBMM-based experimental modal analyses on multi-storey frame structures with different stiffnesses, considering six structural layouts with different configurations of diagonal bracings. The PBMM results, both in terms of time series and identified modal parameters, are validated against benchmarks provided by an array of physically attached accelerometers. In addition, the influence of pixel intensity on estimates’ accuracy is investigated. Although the PBMM method shows limitations due to the low frame rates of the commercial cameras employed, along with an increase in the signal-to-noise ratio in correspondence of bracing nodes, this method turned out to be effective in modal identification for structures with modest variations in stiffness in terms of height. Moreover, the algorithm exhibits modest sensitivity to pixel intensity. An open access dataset containing video and sensor data recorded during the experiments, is available to support further research at the following https://doi.org/10.5281/zenodo.10412857.

Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization

Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization

Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions.

The semantic segmentation (SS) of low-contrast images (LCIs) remains a significant challenge in computer vision, particularly for sensor-driven applications like medical imaging, autonomous navigation, and industrial defect detection, where accurate object delineation is critical. This systematic review develops a comprehensive evaluation of state-of-the-art deep learning (DL) techniques to improve segmentation accuracy in LCI scenarios by addressing key challenges such as diffuse boundaries and regions with similar pixel intensities. It tackles primary challenges, such as diffuse boundaries and regions with similar pixel intensities, which limit conventional methods. Key advancements include attention mechanisms, multi-scale feature extraction, and hybrid architectures combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs), which expand the Effective Receptive Field (ERF), improve feature representation, and optimize information flow. We compare the performance of 25 models, evaluating accuracy (e.g., mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC)), computational efficiency, and robustness across benchmark datasets relevant to automation and robotics. This review identifies limitations, including the scarcity of diverse, annotated LCI datasets and the high computational demands of transformer-based models. Future opportunities emphasize lightweight architectures, advanced data augmentation, integration with multimodal sensor data (e.g., LiDAR, thermal imaging), and ethically transparent AI to build trust in automation systems. This work contributes a practical guide for enhancing LCI segmentation, improving mean accuracy metrics like mIoU by up to 15% in sensor-based applications, as evidenced by benchmark comparisons. It serves as a concise, comprehensive guide for researchers and practitioners advancing DL-based LCI segmentation in real-world sensor applications.

Comparison between FRQI and NEQR quantum algorithms applied in digital image processing

Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.