Noise Correction Research Articles

Charge-coupled device readout by digital-correlated double sampling

Charge-coupled devices (CCDs) play crucial roles in astronomy owing to their widespread use in the optical band, offering high sensibility, low noise, high dynamic range, and high spatial resolution. Recent advancements in CCDs have focused on improving the sub-electron noise levels for particle detection and enhancing readout speeds through simulation studies. The main objective of this study is to replace the analog processing of CCD signals using the digital-correlated double sampling (DCDS) technique. DCDS allows post-acquisition noise correction through intermittent sampling of an internal reference voltage to provide compact and flexible performance. In this study, DCDS was evaluated using two digital processing systems, namely, a 2.5 mega-samples per second (MSPS) 24-bit analog-to-digital converter (ADC) board and a 250 MSPS 16-bit ADC field-programmable-gate-array( FPGA)-based buffer memory board. The readout noise value at 74 kpix/s (7.1 e) was a significant improvement over that obtained with analog processing (9.7 e). The DCDS implementation demonstrates optimal signal-to-noise ratio for a wide range of readout speeds. Parameters such as the sample positions per pixel, number of samples, and number of pixels were identified to be essential in achieving an accurate gain value. Finally, hardware implementation of the DCDS IP core algorithm on a Xilinx Zynq-7000 AP system-on-a-chip (SoC) showed a significant improvement in power dissipation (7.1 W) compared to the analog Monsoon correlated double sample (CDS) circuit (13.6 W). The DCDS IP core implementation also showed a background noise reduction of up to 34% compared to DCDS offline readout processing using a Python algorithm.

Bias Mitigation and Representation Optimization for Noise-Robust Cross-modal Retrieval

The remarkable progress in cross-modal retrieval relies on accurately-annotated multimedia datasets. In practice, most existing datasets used for training cross-modal retrieval models are automatically collected from the Internet to reduce data collection costs. However, it inevitably contains mismatched pairs, i.e. , noisy correspondences, thus degrading the model performance. Recent advances utilize the predicted similarity distribution of individual samples for noise validation and correction, which easily faces two challenging dilemmas: 1) confirmation bias and 2) unstable performance with increasing noise. In light of the above, we propose a generalized B ias M itigation and R epresentation O ptimization framework (BMRO). Specifically, we propose a Bias Estimator (BE) to estimate the unbiased confidence factor of a sample by contrasting it against its nearest neighbors. Unbiased confidence factor can precisely adjust sample contribution and enhance accurate sample division. This facilitates the Adaptive Representation Optimizer (ARO) in providing tailored optimization strategies for clean and noisy samples. ARO performs contrastive learning between clean samples and generated hard samples, thus promoting the generalizability and robustness of the representation. Besides, it utilizes complementary learning to reduce incorrect guidance from noisy samples. Extensive experiments on five visual-text benchmarks verify that our BMRO can significantly improve the matching accuracy and performance stability against noisy correspondences.

Noise emission characteristics of cosmetics manufacturing plants

Recently, a rapid increase in the establishment of cosmetics manufacturing facilities has been observed in Korea because of the growing export of K-beauty products. Many of these factories are certified under good manufacturing practice (GMP) standards and show significant interest in quality management, industrial injury prevention, and productivity enhancement. Among industrial injuries, noise-induced hearing loss holds the largest share, rendering it crucial for industrial injury prevention. Therefore, this study aimed to assess the noise exposure status in cosmetics manufacturing facility spaces. Analysis revealed that the 24-h average continuous noise level (L_eq) was approximately 69.0 dB(A). The measured areas were divided into three rooms based on their purpose, of which the Manufacturing Room showed the highest noise levels ranging from 74.0 to 77.0 dB(A). Additionally, the Blister Packaging and Sealing Room ranged from 64.1 to 66.1 dB(A), and the Box Packing Room ranged from 64.3 to 65.8 dB(A), all within acceptable limits and not exceeding day-night noise correction exposure standards. From the perspective of industrial injury prevention and monitoring, this study aims to implement these measurement results within the metaverse in the future for visualization.

BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction

Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which significantly constrains their implementation in real-world KGs. To overcome this challenge, we propose a novel end-to-end model (BGAT-CCRF) that achieves better noise correction results. Specifically, we construct a balanced-based graph attention model (BGAT) to learn the features of nodes in triples’ neighborhoods and capture the correlation between nodes based on their position and frequency. Additionally, we design a constrained conditional random field model (CCRF) to select suitable candidates guided by three constraints for correcting one or more noises in the triple. In this way, BGAT-CCRF can select multiple candidates from a smaller domain to repair multiple noises in triples simultaneously, rather than selecting candidates from the whole KG to repair noisy triples as traditional methods do, which can only repair one noise in the triple at a time. The effectiveness of BGAT-CCRF is validated by the KG noise correction experiment. Compared with the state-of-the-art models, BGAT-CCRF improves the fMRR metric by 3.58% on the FB15K dataset. Hence, it has the potential to facilitate the implementation of KGs in the real world.

An automatic method for accurate signal-to-noise ratio estimation and baseline correction of Raman spectra of environmental microplastics

In this study, we introduced a k-iterative double sliding-window (DSW^k) method for the estimation of spectral noise, signal-to-noise ratio (SNR), and baseline correction. The performance was evaluated using simulated spectra and compared against other commonly employed methods. Convergent evaluation determined that a k value of 20 strikes an optimal balance between convergence and computational intensity. The DSW^k method demonstrated outstanding performance across different spectral types (flat baseline, baseline with elevation, baseline with fluctuation, baseline with elevation and fluctuation) coupled with SNR values from 10 to 1000, achieving results that ranged from 1.01 to 1.08 times of the reference value in estimating spectral noise. It also showed that the estimated SNR values are 0.89 to 0.93 times of the reference value, demonstrating a 74.5 % − 131.7 % improvement over the conventional method in spectra with elevated and/or fluctuating baselines. Additionally, the DSW^k method proved effective in correcting baselines and identifying polymers in environmental samples of polyethylene (PE), polypropylene (PP), and polystyrene (PS), despite the limitation of reducing the peak height in spectra with low SNR. This method offers the potential to enhance the automatic and accurate evaluation of spectral quality and could assist in the development of guidelines for more rapid parameter adjustments in Raman measurements.

Joint Bayesian estimation of process and measurement noise statistics in nonlinear Kalman filtering

Bayesian filtering aims at sequentially estimating the states and parameters in nonlinear dynamical systems, and finds applications in a variety of engineering fields. Extended and unscented Kalman filter are popular choices for this purpose as they typically achieve a good trade-off between computational complexity and accuracy, while still quantifying posterior uncertainty. However, the accuracy of the identified states and parameters, both in terms of their posterior mean and variance, is heavily influenced by the assumed statistics of both the process and measurement noise terms. Therefore, learning the noise characteristics becomes an essential aspect of Bayesian filtering, but is non-trivial especially in cases where process and measurement noise statistics are jointly non-identifiable. This article proposes a methodology that decomposes measurement and process noise learning in a fully Bayesian setting. For measurement noise estimation, we improve upon a Bayesian method that leverages conjugacy of the inverse-Wishart distribution with respect to the Gaussian likelihood model. For process noise estimation, Bayesian model averaging is coupled with a mutation scheme to compare the performance of competing models while exploring high-probability regions in the space of the unknown process noise variance. The algorithm is first tested on simple numerical models for both state estimation and parameter learning, highlighting the superior accuracy of the proposed measurement noise correction and the benefits of decoupling process and measurement noise learning in non-identifiable settings. Then we demonstrate the usefulness of this algorithm in more challenging problems, namely identification of a multi-degree-of-freedom system under unmeasured excitation, and modeling of the highly nonlinear response of a full-scale bridge pier using experimental data.

Revised LOFAR upper limits on the 21-cm signal power spectrum at z ≈ 9.1 using machine learning and gaussian process regression

ABSTRACT The use of Gaussian Process Regression (GPR) for foregrounds mitigation in data collected by the LOw-Frequency ARray (LOFAR) to measure the high-redshift 21-cm signal power spectrum has been shown to have issues of signal loss when the 21-cm signal covariance is misestimated. To address this problem, we have recently introduced covariance kernels obtained by using a Machine Learning based Variational Auto-Encoder (VAE) algorithm in combination with simulations of the 21-cm signal. In this work, we apply this framework to 141 h (${\approx} 10$ nights) of LOFAR data at $z \approx 9.1$, and report revised upper limits of the 21-cm signal power spectrum. Overall, we agree with past results reporting a 2-$\sigma$ upper limit of $\Delta ^2_{21} \ \lt\ (80)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. Further, the VAE-based kernel has a smaller correlation with the systematic excess noise, and the overall GPR-based approach is shown to be a good model for the data. Assuming an accurate bias correction for the excess noise, we report a 2-$\sigma$ upper limit of $\Delta ^2_{21} \ \lt\ (25)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. However, we still caution to take the more conservative approach to jointly report the upper limits of the excess noise and the 21-cm signal components.

Van der Waals mid-wavelength infrared detector linear array for room temperature passive imaging.

Passive imaging for mid-wave infrared (MWIR) is resistant to atmospheric pollutants, guaranteeing image clarity and accuracy. Arrayed photodetectors can simultaneously perform radiation sensing to improve efficiency. Room temperature van der Waals (vdWs) photodetectors without lattice matching have evolved rapidly with optimized stacking methods, primarily for single-pixel devices. The urgent need to implement arrayed devices aligns with practical demands. Here, we present an 8 by 1 black phosphorus/molybdenum sulfide (BP/MoS2) vdWs photodetector linear array with a fill-factor of ~77%, fabricated using a temperature-assisted sloping transfer method. The flat interface and uniform thickness facilitate carrier transport and minimize pixel nonuniformities, showing an average peak detectivity (D*) of 2.34 × 109 cm·Hz1/2·W-1 in the mid-wave infrared region. Compared to a single pixel, push-broom scanning passive imaging is eight times more efficient and further enhanced through mean filtering and fast Fourier transform filtering for strip noise correction. Our study offers guidance on vdWs arrayed devices for engineering applications.

A 18-bit 1-MS/s fully-differential SAR ADC with digital calibration achieving 96.1 dB SNDR

This paper presents a 18-bit 1 MS/s fully-differential Successive-Approximation-Register Analog-to-Digital Converter (SAR ADC) achieving an ENOB of 15.7-bit. To achieve higher performance, a differential DAC utilizing both split and monotonic switching timing is designed. For both dynamic and static performance improvement, five techniques are proposed. First, a foreground digital self-calibration method based on normalized-full-scale-referencing is described to eliminate the capacitor mismatch errors. L-segment capacitors are utilized to measure and calculate the bit weights of other capacitors. Second, a DNL enhancement technique is presented. The fractional capacitors are used to subtract an analog voltage from the DAC before the conversion is finished, which further improve the ADC performance. Third, an adaptive-tracking-extra-bit-trail together with comparator noise extraction and correction for further accuracy enhancement is proposed. Forth, a harmonic calibration technique, which can efficiently attenuate 2-order and 3-order harmonic is introduced. Fifth, a comparator with ultra-low noise and offset is designed to meet the 18-bit 1-MS/s ADC. The ADC is fabricated in a 0.18-μm 5-V CMOS process. It measures a 96.1 dB SNDR and a 110.7 dB SFDR. The DNL and INL are within ±0.32 LSB and ±0.5 LSB, respectively. The overall power consumption of ADC core, drawn from the 5 V power supply, is 45 mW.

Sampling effects on background noise corrections

Background noise corrections are used to estimate true source levels from measured source levels. Use of a background correction involves determining the background noise from measured samples. Previous work explored the effect of sampling on the statistical background noise correction through the steadiness parameter. In this paper the effect of sampling on the statistical and legacy background noise corrections through the steadiness and source-background level difference parameters is investigated. Samples of background noise are drawn from a chi-square distribution, which has been shown to describe background noise arising from multi-path or multi-mode processes. Background mean and variance are determined from a specified number of background noise samples, and statistical distributions and confidence bands are formed. The distributions and confidence bands of the statistical and legacy background noise corrections are compared and are used to identify parameter ranges that promote accuracy of source levels obtained with background noise corrections. Specific limits on these parameters are recommended for the statistical background noise correction. The benefits and advantages of the statistical background noise correction are identified, and a path towards standardizing the statistical background noise correction is proposed.

Spline-based Abel Transform (SAT) radial property reconstruction for noise and trapping correction: Application to axisymmetric sooting flames

In combustion research, optical diagnostics often necessitate Abel inversion to deconvolve the measurements of axisymmetric flames, especially to determine local soot temperature fields from integrated flame radiative emission measurements. However, traditional Abel inversion methods can considerably amplify experimental noise, especially towards the flame centerline. Although various strategies exist to mitigate this issue, they typically do not address the correction of signal trapping within the flame. The present paper introduces an adapted Abel inversion tool that combines resistance to experimental noise with a mechanism to correct signal trapping effects. The tool employs noise-free Abel inversion using piecewise spline functions. Its effectiveness is validated through numerical comparisons with conventional methods. The practical application is demonstrated in the measurement of soot temperature in a canonical laminar diffusion ethylene flame. This is achieved by using multi-wavelength line-of-sight attenuation (LOSA) and emission measurements. The tool corrects for the signal trapping effect in emission data by integrating extinction profiles, thus enabling a more precise soot temperature analysis. The current study also compares two techniques of temperature measurement based on thermal emission: traditional two-color pyrometry and a calibrated emission and absorption ratio method. The study further explores the effects of signal trapping, scattering contribution, and the absorption function ratio on soot temperature determination based on experimental profiles. The findings suggest that signal trapping and scattering contributions have a negligible impact on measurements at the wavelengths examined. However, the choice of absorption function ratio significantly influences the outcomes of two-color pyrometry, highlighting the advantage of the one color emission/absorption technique for the determination of sooting flames temperature measurements.

An improved correction of radial velocity systematics for the SOPHIE spectrograph

High-precision spectrographs can on occasion exhibit temporal variations in their reference velocity or nightly zero point (NZP). One way to monitor the NZP is to measure bright stars, whose intrinsic radial velocity variation is assumed to be much smaller than the instrument precision. The variations of these bright stars, which is primarily assumed to be instrumental, are then smoothed into a reference radial velocity time series (master constant) that is subtracted from the observed targets. While this method is effective in most cases, it does not fully propagate the uncertainty arising from NZP variations. We present a new method for correcting for NZP variations in radial velocity time series. This method uses Gaussian processes based on ancillary information to model these systematic effects. Moreover, it enables us to propagate the uncertainties of this correction into the overall error budget. Another advantage of this approach is that it relies on ancillary data that are collected simultaneously with the spectra and does not solely depend on dedicated observations of constant stars. We applied this method to the SOPHIE spectrograph at the Haute-Provence Observatory using a few instrument housekeeping data, such as the internal pressure and temperature variations. Our results demonstrate that this method effectively models the red noise of constant stars, even with a limited number of housekeeping data, while preserving the signals of exoplanets. Using simulations with mock planets and real data, we found that this method significantly improves the false-alarm probability of detections. It improves the probability by several orders of magnitude. Additionally, by simulating numerous planetary signals, we were able to detect up to 10% more planets with small-amplitude radial velocity signals. We used this new correction to reanalyse the planetary system around HD158259 and to improve the detection of the outermost planets. We propose this technique as a complementary approach to the classical master-constant correction of the instrumental red noise. We also suggest to decrease the observing cadence of the constant stars to optimise the telescope time for scientific targets.

SCIENTIFIC APPROACHES TO THE DEVELOPMENT OF COMPOSITES MATERIALS FOR REDUCING VIBRATION AND NOISE IN PRODUCTION

Goal. Scientific approaches to the development of composite materials for noise and vibration reduction in the workplace and evaluation of their effectiveness. Methodology. Сomplex of scientific methods for assessing the impact of environmental factors, such as noise and vibration, on the worker was used for the research. Сomparative evaluation of the effectiveness of the developed composite materials with the existing ones was carried out in terms of their ability to reduce noise and vibration at the worker's workplace. Results. New composite materials with an extended spectrum of action have been developed. Their effectiveness in laboratory and production conditions was evaluated. Their effectiveness was evaluated at those workplaces where an increased level of noise and vibration is registered, a comparative analysis of the new vibration-absorbing materials developed by the authors with those existing in laboratory and production conditions was carried out.Conducted studies have shown that «Vibroshtil» and «Vibroshtilmaxi» vibration-absorbing materials are characterized by a higher level of sound absorption compared to both domestic and materials of the same purpose produced in other countries, and are proposed for implementation in production, railway transport and the subway. Conducted studies have shown that «Vibroshtil» and «Vibroshtilmaxi» vibration-absorbing materials are characterized by the higher level of sound absorption compared to both domestic and materials of the same purpose produced in other countries, and are proposed for implementation in production, railway transport and the subway. The comparative evaluation made it possible to prove the effectiveness of the developed composite vibration-absorbing materials for the correction and reduction of noise and vibration levels in workplaces. The results were achieved due to the optimal selection of ingredients of composite materials and their ratio. Scientific novelty. The effectiveness of new composite materials «Vibrostil» and «Vibrostilmax» for reducing noise and vibration levels at workplaces was developed and evaluated, and their effectiveness was evaluated in order to prevent the risk of workers suffering from vibration sickness. Practical significance. The introduction of developed vibration-absorbing materials will reduce the level of noise and vibration in the workplace and reduce the risk of workers suffering from vibration sickness.

Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module

The error correction model’s main purpose in heavy hexagonal quantum codes is to improve their reliability for quantum computing applications. Existing challenges include finding the optimal decoder for quantum error correction in heavy hexagonal codes. This research propels the frontier of quantum error correction, with a specific focus on tailoring topological quantum error-correcting codes for the unique challenges posed by superconducting qubits in quantum computers. In response, this research harnesses the power of deep learning, presenting a Humming sparrow optimization based self-adaptive deep CNN (HSO-based SADCNN) model designed for heavy hexagonal codes. This decoder incorporates a Self-adaptive Deep CNN (SADCNN) Noise Correction Module, a sophisticated component to refine error correction. The proposed decoder’s efficacy is rigorously evaluated across varying code distances (three, five, and seven) using the Humming Sparrow Optimization (HSO) algorithm. HSO, intricately designed to fine-tune the SADCNN decoder, significantly enhances its error correction capabilities for heavy hexagonal quantum codes. The algorithm seamlessly integrates advantageous characteristics of herding and tracing from Humming Bird optimization and Sparrow search optimization, representing a critical stride in advancing the reliability of quantum computing applications, particularly within the intricate domain of heavy hexagonal quantum codes. Based upon the achievements, the Training Percentage (TP) 90 metrics demonstrate significant progress, boasting a commendable accuracy of 97.35%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.35\\%$$\\end{document}, coupled with reduced logical error probability and a diminished bit error rate, marked at 5.51 and 3.72, respectively.

Advances in functional and structural imaging of the brainstem: implications for disease.

The brainstem's complex anatomy and relatively small size means that structural and functional assessment of this structure is done less frequently compared to other brain areas. However, recent years have seen substantial progress in brainstem imaging, enabling more detailed investigations into its structure and function, as well as its role in neuropathology. Advancements in ultrahigh field MRI technology have allowed for unprecedented spatial resolution in brainstem imaging, facilitating the new creation of detailed brainstem-specific atlases. Methodological improvements have significantly enhanced the accuracy of physiological (cardiac and respiratory) noise correction within brainstem imaging studies. These technological and methodological advancements have allowed for in-depth analyses of the brainstem's anatomy, including quantitative assessments and examinations of structural connectivity within both gray and white matter. Furthermore, functional studies, including assessments of activation patterns and functional connectivity, have revealed the brainstem's roles in both specialized functions and broader neural integration. Notably, these investigations have identified alterations in brainstem structure and function associated with various neurological disorders. The aforementioned developments have allowed for a greater appreciation of the importance of the brainstem in the wider context of neuroscience and clinical neurology.

Speckle noise detection and correction for frequency-scanning interferometry in vibration measurement

Speckle noise significantly impairs the accuracy of vibration measurements for non-cooperative targets in frequency-scanning interferometry (FSI). To restore the real vibration, this paper proposes a comprehensive vibration demodulation algorithm based on the FSI. We established an FSI vibration model accounting for speckle noise. The extraction of instantaneous frequency from the interference signal is performed using a complex-shifted Morlet wavelet. An autoregressive differential model, incorporating a smooth weight function, is applied for bidirectional prediction to correct speckle noise. Finally, the true vibration of the target is restored through Kalman filtering. We validated the algorithm’s accuracy using a Monte Carlo strategy. The experimental results show that the surface vibration of black aluminum oxide alloy and the axial runout of the DC motor during operation was successfully captured, and the time–frequency analysis results showed that this method has strong robustness against dense speckle noise.

On the accuracy bounds of high-order image correlation spectroscopy

High-order image correlation spectroscopy (HICS) or related image-based cumulant analysis of emitter species are important for identifying properties and concentrations of biomolecules or nanoparticles. However, lack of a thorough parameter space test limits its use in full potential. The current study focused on mapping accuracy bounds of bimodal species concentration space by simulating and analysing more than 2 × 105 images (∼1011 data points). Concentration space maps for four values of quantum yield contrast ratio between two species in a mixture and two sampling spaces (834 and 13357 beam areas in an image) were created, which showed clear accuracy bounds governed by two factors, Poisson fluctuation and quantum yield ratio. Typically, brighter species concentration was 1-3 orders of magnitude lower than that of dimmer species, and higher brightness contrast allowed higher concentration difference. Upper limit of accuracy bounds was governed by resolvable Poisson fluctuation, where this condition was violated for emitter density beyond 10 particles per beam area. The accuracy bounds are shown to be largely invariant under noise correction or the calculation method, and are compared against previous experimental results, showing consistent agreement. This study shows that concentration limit needs to be observed when using HICS or related image moment or cumulant analysis techniques. As a rule of thumb, a large quantum yield contrast and large sampling points allow more concentration difference between two species to be resolved in an analysis.

Development of web-based software for environmental impact assessment focusing on road traffic noise

In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.

Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

The non-uniformity inherent in infrared detectors’ readout circuits often manifests as stripe noise, significantly impacting the interpretability and utility of infrared images in remote sensing applications. This paper introduces a novel three-step approach designed to overcome the challenges posed by stripe noise, offering a balance between real-time performance, detail preservation, and noise suppression. The proposed method involves subtracting the average of image columns from the noisy image, adding the wavelet denoised average signal to the subtraction result, and finally correcting the resulting image using an image-guidance mechanism. This unique three-step process ensures effective noise removal while preserving image details. The incorporation of wavelet transform leverages the sparsity of noise in the wavelet domain, enhancing denoising without introducing blurring. In a further refinement, the third step utilizes an image-guidance mechanism to recover small details with increased precision. This comprehensive approach addresses both stripe noise and non-uniformity, offering an easy, efficient, and fast technique for image correction. A comprehensive set of experiments, which involves comparisons with state-of-the-art algorithms, serves to substantiate the effectiveness and superior performance of the proposed method in real-world remote sensing and infrared images. Various examples, encompassing both real and artificial noise, are presented to showcase the robustness and applicability of our approach.

Noise correction in differential phase contrast for improving phase sensitivity

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.