IRFPA strong nonuniformity adaptive correction algorithm based on adjacent pixel differential statistics

An adaptive correction algorithm is demonstrated based on weak measurement, which introduces a feedback and an additional interaction to the system and can dynamically adjust the operating point in accordance with the condition of the estimated phase change. Two schemes, fast adaptive correction and slow adaptive correction, are proposed for different conditions of the modulation device. Fast adaptive correction scheme can realize a real-time correction and maintain the high sensitivity. Slow adaptive correction scheme, as a supplement, can correct the distortion of the measured parameter by changing the measuring period. These two schemes are useful for high precision phase estimation with time in modern physics and practical applications, including, but not limited to, timing synchronization, accurate distance measurement, and gravity wave detection. Moreover, we discuss the deviation of the adaptive correction for considering system noise in practical measurement.

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.

As a new display technology, Mini‐LED has more and more broad prospects. However, there are some difficult problems, such as Mass Transfer, yield, Uniformity of brightness and chroma. In order to improve the uniformity, especially the low grayscale, we propose a fine full grayscale correction method, which includes precise pixel‐by‐pixel brightness and chroma extraction algorithm and level‐by‐level full grayscale correction algorithm. To ensure the uniformity of the interior of the cabinet and between the cabinets, the full‐color viewing angle compensation and adaptive cabinet edge seam correction algorithm is also proposed, which can realize the arbitrary splicing of the cabinets.

With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is introduced, and involves the compare result between improved neural networks and other adaptive correction techniques. A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful in future infrared imaging systems.

To obtain high-quality imaging data stably under the influence of variable abnormal illumination, a fast adaptive exposure correction algorithm based on optimal niche is proposed in this paper. Firstly, an image sensory quality assessment based on Harr-Wavelet (HW-ISQA) is proposed as an imaging quality assessment indicator to reflect human visual sensory quality objectively. Secondly, an image enhancement algorithm for uneven illumination. Then, an online optimization framework based on the optimal niche can be used to optimize multiple parameters in real-time system. Finally, HW-ISQA is used as the fitness function in the proposed optimization framework to optimize sensitive parameters in the imaging correction our proposed, the imaging quality of various scenes can be improved adaptively, meanwhile, the high-quality video data can be output timely. The performance and efficiency of the algorithm are illustrated by abundant experimental results, while, the superiority and universality of the algorithm in complex scenarios are verified.

The control of each individual bend and overall process is presented in Part 1 of the paper. In Part 2 of the paper, statistical methods are used to analyze and improve 3-D tube bending accuracy. The relationship between bending process error and tube geometry error is obtained with Monte Carlo simulation. For the same tube tolerance requirement, the required process tolerance varies in a large range based on tube geometry. Among the three bending errors: bend angle, bend plane and distance between bends, bend angle error has the largest influence on tube error. For a tube with multiple bends, the overall tube geometry error can be minimized by intentionally modifying the nominal values of the bends to be made based on the errors in the existing bends. The required modification of the bending commands is calculated with an adaptive bend correction algorithm.

The article focuses on the development and implementation of an effective methodology for measuring high-precision parts on coordinate measuring machines (CMM). The proposed approach addresses the challenges associated with complex geometry measurements under variable environmental conditions by combining advanced mathematical modeling techniques with adaptive error compensation algorithms. The mathematical foundation is based on the application of tensor formalism in Riemannian space, which allows for more precise model-ing of geometric errors using Christoffel symbols and covariant derivatives of the error po-tential. This approach significantly improves the accuracy of spatial positioning. A refined stochastic error model, incorporating Stratonovich integrals and fractional Brownian motion, provides a more accurate description of random processes occurring in the measurement sys-tem. To enhance overall measurement accuracy, an adaptive correction algorithm is pro-posed, based on Itô stochastic differential equations with Fourier-Bessel series expansion, which ensures efficient compensation of systematic errors. In addition, the measurement tra-jectory optimization is formulated as a variational problem, taking into account holonomic and non-holonomic constraints, enabling the optimal positioning strategy. A thermoelastic deformation model based on sixth-rank tensors and Green’s functions is developed to account for temperature-induced deformations, ensuring the reliability of measurements under ther-mal instability. Experimental verification confirmed the effectiveness of the proposed method-ology, demonstrating a 15-20% reduction in systematic errors, a 10-15% decrease in random errors, and an approximately 20% improvement in the accuracy of measurement uncertainty estimation. The proposed methodology combines theoretical advancements with practical so-lutions, providing a robust tool for improving the accuracy and reliability of coordinate measurements in industrial metrology applications.

On the basis of the analysis of the effect of PHase Noise (PHN) and Common Phase Error (CPE) on Orthogonal Frequency Division Multiplexing (OFDM) systems, a cost function is constructed. By the cost function and the idea of Least-Mean-Square (LMS) adaptive algorithm, the adaptive algorithm for the correction of CPE is presented. The simulations have been performed to investigate the performance for tracking PHN and estimating CPE, the results show that the algorithm performs soundly.

Fast adaptive phase unwrapping algorithm based on improved bucket sorting

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.

Calculation of a subject-specific adaptive motion-correction factor for improved real-time navigator echo-gated magnetic resonance coronary angiography.

An adaptive kernel correlation filter algorithm and synchronization error correction method for 3D motion reconstruction of flexible flapping wings using binocular vision

This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter.

One of the fundamental and significant distinctiveness of an image is that, adjacent pixels are extremely correlated. The spatial information in the image improves the quality of clustering which is not utilized in the standard Fuzzy C-Means (FCM). FCM algorithm is not robust against noise. In this paper, we proposed an enhanced version of Fuzzy C-Means algorithm that incorporates spatial information into the membership function for clustering of brain MR images. The modified Fuzzy C-Means finds optimal clusters in an automatic way with the help of some cluster validity criteria. Additionally, spatial weighted information is incorporated in the spatial FCM. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. The advantages of this new method are: (a) it yields regions more homogeneous than those of other methods and (b) it removes noisy spots. It is less sensitive to noise as compared to other techniques. We tested our method on various brain MR images, and the technique has proved as a powerful method in the segmentation of noisy images.

A general iterative method is described for the problem of optimizing a nonlinear function of multiple parameters subject to a system of linear constraint equations. This scheme, derived by Frost (1972), is called the 'corrective gradient projection (CGP)' method. It is obtained by modifying Rosen's (1960) 'gradient projection (GP)' method to correct for any deviation of the parameters from the constraint equations.The CGP method can be applied to the problem of multichannel tapped-delay-line (TDL) sensor array processing as used in the fields of seismic, radar and acoustic communications. A variety of algorithms associated with signal processing problems--such as those developed by Frost (1972), Rosen (1960), Booker and Ong (1971), Widrow et al (1967), and Griffiths (1969)--can be derived from or related to the CGP method. They are obtained by making some basic assumptions and statistical approximations.Comparative simulation experiments have been made using a digital computer to examine the performance of some of the algorithms for real seismic data. The algorithms examined are the constrained (unbiased) adaptive algorithm of Frost (1972), the unconstrained adaptive algorithm of Widrow et al (1967) and the constrained (unbiased) optimum processor (Claerbout, 1968; Frost, 1972). The adaptive algorithms are capable of discriminating against noise signals in real time. The Frost algorithm strictly maintains a specified constrained frequency response for the array, while the Widrow algorithm alters the equivalent array response during the adaptations. For the Frost algorithm, the adaptive processor approaches the optimum one as the number of adaptations increases because it progressively learns the noise statistics.