Method of detecting and eliminating pulse noise when processing images and apparatus realising said method

FIELD: information technology.

SUBSTANCE: method of detecting and eliminating pulse noise when processing images involves comparing values of the original digital image with different threshold values. A set of penalties is then created for values of the original digital images exceeding the threshold values. The resultant penalty values are formed by adding separate penalty values for each reading. Readings whose resultant penalty values exceed the calculated threshold level are considered abnormal. Further, two-dimensional arrays of penalties are formed for each threshold level. Two-dimensional nonstationarity regions are determined and localised by a two-dimensional programmed detector with subsequent zeroing of the penalty values. The detected pulse noise values are eliminated by replacing them values of a first-order approximating surface on the localised regions.

EFFECT: detection and elimination of pulse noise values when processing digital images in conditions of non-parametric expected uncertainty of statistical characteristics of pulse noise and the image.

2 cl, 4 dwg

 

Digital image processing is an important area of application of the modern digital computer technology, with the aim of solving problems of automated management and control, improving visual quality, increase reliability of estimates of parameters of objects and structures. The majority of digital processing methods for the extraction of one-dimensional signals against additive noise, in some cases, can be generalized to the multidimensional case, which include the signals of static images. In the process of transmission and transformation through radio systems images are exposed to different noise, which in some cases leads to deterioration of visual quality and the loss of image regions (groups of pixels). In this regard, the main task is to restore the image attenuation of the noise, restoration (removing scratches, spots, dust and other defects) and extrapolation of image pixels.

When solving problems of image processing with the aim of weakening of the existing additive noise is considered a model image, which represents a two-dimensional discrete sequence of Yi,j,,type:

where Si,juseful two-dimensional component (original undistorted image is agenie), ηi,jadditive noise component, ei,j- value impulse noise, N is the number of rows, M is the number of columns of the two-dimensional array image.

The purpose of this invention is the detection values of the impulse noise εi,jin the implementation of digital image Yi,jand Troubleshooting in case of insufficient a priori information about the statistical characteristics of the additive ηi,jand impulse noise εi,j. It is assumed that the statistical characteristics of the additive noise ηi,jand impulse noise εi,jdiffer significantly.

The image can be considered as a rectangular matrix Y={Yi,j}, with rows i and columns j, where N and M determine the matrix size of the image in pixels. One of the primary tasks in digital signal processing is the weakening of existing additive and multiplicative impulse noise (1). To solve these problems are widely used methods of nonlinear processing based on rank statistics, quasi-optimal estimation signals, etc. [Gonzales, Rwoods, Digital image processing. M: Technosphere, 2005, - S. 1072]. Non linear processing is reduced to the construction of nonlinear spatial filters based on rank statistics. The response of the filter is determined by preliminary ranking of the values of the pixels is LEU, covered by the mask filter, and then select the value on a specific position in an ordered sequence [J. Astola, P. Kuosmanen Fundamentals of nonlinear digital filtering // Boca Raton (USA): CRC Press LLC, 1997. - P.276; I. Pitas, A.N. Venetsanopoulos Nonlinear Digital Filters // Boston (USA): Principles and Applications. Kluwer Academic Publisher, 1990. - P.321; Huang T. image Processing and digital filtering // M.: Mir, 1979 - S; Taguchi, A., M. Meguro Adaptive L-filters Based on Fuzzy Rules // San Jose (California, USA): Proc. of IS&T/SPIE Symposium on Electronic Imaging. Science and Technology. - 1995, V.2424. - P.76-83]. One example of the current impulse noise is noise, which describes the Rayleigh distribution or Weibull.

Known methods of nonlinear filtering, which include methods based on sequence statistics (median filtering, weighted median filtering, ways marianowo assessment L assessment R assessment, M evaluation etc.) [EN 2045129, EN 2043654, SU 1698896].

In the case of the median filter uses a filter mask having a Central symmetry, the center is located at the current point filtering. The effect of the median filter is to replace the values in the Central region of the mask image on the median brightness values in the neighborhood of the filter mask. To assign the elements covered by the mask filter, the different scales used weighted median filter [I. Pitas, A.N. Venetsanopoulos Nonlinear Digital Filters // Boton (USA): Principles and Applications. Kluwer Academic Publisher, 1990. - P.321]. The values of the coefficients decrease as the distance from the center of the mask.

The advantages of the median filtering is the ability to eliminate impulse noise with less smoothing of the brightness difference of the image, in comparison with linear filtering.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: processing of two-dimensional digital signal, storing the digital signal, eliminating value impulse noise.

The disadvantages of the known method and device, it implements are:

the dependence of the efficiency of noise reduction of the size of the filter mask;

weak noise with a Gaussian distribution.

- requires a priori information about my, σyand the law of distribution.

To non-linear processing methods also include filter the midpoint, which combines methods of order statistics and averaging [Gonzalez, R., woods, R. Digital image processing // M: Technosphere, 2005. - S]. The operation of this filter is to calculate the average between the maximum and minimum values in the filter:

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: processing of two-dimensional qi is the global signal, storing digital signal, eliminating value impulse noise.

The disadvantages of the known method and device, it implements are:

- inability to handle the boundary rows or columns of the image.

Widely used methods based on ranking criteria L-, M - and R-estimates [Huber GP Robustness in statistics // TRANS. from English. - M.: Mir, 1984. - S].

L-evaluation. This class of nonlinear processing methods uses the ordinal statistics and weighted sum. The score achieved by the L-filter is described by the following expression [Processing of random signals and processes / Besedin A.N., Zelensky A.A., G.P. Kulemin, Lukin V.V. - tutorial. - Kharkiv: National. Aerocom. University of Hark. Aviat. Inst.", 2005. - S. 469]:

where Y(i+s,j+f)- sequence statistics for the source image pixels belonging to a moving window of size m×n, ws,j- weighting factor.

Selecting the values of weight coefficients ws f- can be changed in wide aisles properties of L-estimates. By varying the coefficients of ws fyou can reach a compromise between resistance to impulse noise while preserving sharp drops image and smoothing Gaussian noise [Processing of random signals and processes / Besedin A.N., Zelensky A.A., G.P. Kulemin, Lukin V.V. tutorial. - Kharkiv: National. Aerocom. University of Hark. Aviat. Inst.", 2005. - S. 469].

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: processing of two-dimensional digital signal, storing the digital signal, eliminating value impulse noise.

The disadvantages of the known method and device, it implements are:

it is impossible to make the optimal or quasi-optimal estimation of weights ws fin terms of non-parametric a priori uncertainty.

There is a method of digital processing of digital images when solving problems removing impulse noise based on the R-estimates [Huang T. image Processing and digital filtering // M.: Mir, 1979. - S]:

where Rs f- the rank of the observation image; ws f- weights.

The representative of the class R-score is a score based on the ranking criterion of Wilcoxon [Processing of random signals and processes / Besedin A.N., Zelensky A.A., G.P. Kulemin, Lukin V.V. - tutorial. - Kharkiv: National. Aerocom. University of Hark. Aviat. Inst.", 2005. - 469 S.; Angelinskiy, VNIISI. Engineering Express-analysis of random processes. - M.: Energy, 1979. - 113 C.].

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: education is such a two-dimensional digital signal, storing digital signal, eliminating value impulse noise.

The disadvantages of the known method and device, it implements are:

- a significant drawback evaluation Wilcoxon is a large algorithmic complexity of its receipt, and even with relatively small sample sizes N problems of providing the necessary performance computing;

- the use of R-ratings Wilcoxon little effective for smoothing noise with a Gaussian density distribution.

A method of processing digital images when solving problems removing impulse noise based on M-estimation. The method based on M-estimates is a solution of the problem of maximum likelihood in a sliding window [Grozman I.S., Kirichuk B.C., Oblique VP, Peretyagin GI Digital image processing in information systems // Novosibirsk: Izd-vo NSTU, 2000. - S]. The processing method based on M-estimates summarizes the methods of processing on the basis of L-estimates and R-estimates.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: processing of two-dimensional digital signal, storing the digital signal, eliminating value impulse noise.

The disadvantages of the known method and device, it implements are:

in conditions of limited size scorsesean when digital imaging solution of the problem of maximum likelihood is significantly hindered, which leads to the transition to quasi-optimal estimates;

- the absence of a priori information about the distribution density of the signal undistorted image and the current impulse noise is not possible to obtain an M-evaluation.

Methods of processing images based miriany estimates. As for solving the problem of maximum likelihood searches myriad sampling [Kalluri, S., Arce G. Adaptive weighted myriad filter algorithms for robust signal processing in α-stable noise environments // Proc. of IEEE Trans, on signal processing, 1998. - V.46. - No 2. - P.322-334.]:

where k>0 is a tuning parameter called parameter linearity myriad, which plays a fundamental role in theory miriamlugo assessment. When k≤2σW, miriany filter has the ability to suppress mixed interference and to keep a sharp drop images and their properties assessment similar to the assessment of the median filter. When k>>σWproperties miriamlugo filter close to the properties of the estimates of a simple moving average [Abramov S. Kaliev Algorithm implementation miriade /filtering/ aerospace technics and technology - VIP. - Kharkiv: National. aerospace University "Khai", 2000 - P.143-145].

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: robust processing of two-dimensional digital signal, storing the digital signal is a, fixing values of impulse noise.

The disadvantages of the known method and device, it implements are:

to achieve the required compromise between attenuation of the noise and obtain a smooth estimate of the useful component of the necessary a priori to estimate or know the statistical characteristics of the components of the processed signal.

the operation of finding the minimum of the objective function (5), which is quite time-consuming, significantly limits its application.

Closest to the invention is a method of detecting anomalous measurements without evaluating the function of trend and device for its realization [U.S. Pat. No. 2302655, IPC G06F 15/00]. A set of finesfor discrete values of the initial implementation of the measurement resultsthat exceeded the established thresholds yk≥pi,defined intervals nonstationarity penalty values with their subsequent zeroing, are formed resulting penalty values by summing the values of fines for each count of the original discrete implementation of the measurement results, the samples of the original discrete implementation, in which the resulting penalty values exceed the estimated threshold level are considered abnormal .

The characteristics of the prototype method, coinciding with the characteristics of the proposed technical solution, the following: formation thresholds, storage of results, the discriminator, zeroing intervals nonstationarity penalty values, zero, adder.

The disadvantages of the known method and device, it implements are:

- no available methodology for determining thresholds for each phase of the fining values;

- not able to process two-dimensional signals and images.

The structural scheme of the device that implements the algorithm, contains a block of storage of the measurement results, the unit set the number of thresholds, the block formation thresholds, blocks discrimination storage units penalty values, the setting unit interval detection units detect gaps nonstationarity penalty values, blocks of zero, the summation block, the block threshold, the block discriminator, a storage unit, the processor clock.

The proposed method for the detection and removal of impulsive noise in image processing assumes that the source of the processed digital image describes a nonstationary random process, which is an additive mixture of noise and undistorted image, multiplied by a binary switching function describing the presence of the normal values (the values of the impulse noise):

where λi,jvalues undistorted digital images of size N×M;

εn(i,j)values of the additive noise; ai,j- realization of a binary random switching function that accepts with probability PENthe value of ai,j=1 and with probability (1-PEN) is ai,j=0; RENis the probability of occurrence of abnormal values, PEN<<1. Mathematical model of the original image signal (6) is a special case of model (1).

Relative to the model undistorted digital signal λi,jit is assumed that some of the closed areas it can be quite accurately described by the surfaces of the first order. Relative values of the additive noise εn(i,j)it is expected that its implementation provides independent samples, and the density distribution is symmetric with respect to the mathematical expectation in any section.

The analyzed signal is an 8-bit digital image grayscale (6). Determine the maximum amplitude of the signal and divide it by the specified number of levels P. as is the uniform splitting of the signal P levels (thresholds):

where Δz=(max(z)-min(z))/P is the length of the signal.

Figure 1 presents an example of partitioning the implementation of the two-dimensional what about the signal threshold levels L s. It is hypothesized that if for some xed values of Lsthere will be some closed region D of a fixed size, in which each element is executed, the condition zi,j≥Lsthis region is an area of non-implementation of two-dimensional signal and should be localized.

Based on the threshold values of Lsformed s arrayof dimension N×M.

Arraysrepresents a collection of values (0 or 1), who are appointed in accordance with the following condition:

where D is a closed non-zero area of a square or rectangular arrayThe size of the area D is the area of non-processed signal and limits the accumulation values of the penalties in subsequent arrays(figure 2).

Figure 3 presents a graphical explanation of the conditions for the formation of arraysbased on the expression (8). For each fixed value of Lscreates an arraywhile.

Region D is determined by the size and is the basis of two-dimensional digital software of the detector. the simplest case, the shape of the domain D is square or rectangular. When forming the arraytests two conditions zi,j≥Ls,The first condition checks whether the value of the array element of the original two-dimensional signal zi,jthe threshold Ls. The second condition checks the identity of the analyzed element in the array to the field of nonstationarity D, formed in the previous step s-1. The value of the arrayonly if both conditions are met: zi,j≥Ls,As a result of performance conditions (8) and the formation of arrayswhenis localised areas of nonstationarity of the original two-dimensional signal the size of the region D and more.

The result arrayis formed by averaging element in accordance with the following expression:

The position of anomalous values in the original implementation of the two-dimensional signal is determined from the analysis of arrayby checking the conditions:

Thus, the arraythat represents the mask layout anomalous values in the source i.e. monitoring) reference and two-dimensional signal.

In the original formulation, it was noted that the original signal is locally smooth (theorem of Weierstrass). To correct the values of the impulse noise, we define the local area with regard to them and carry out the approximation values of the surface given order. For solving this task, you must determine the size of the localized surface, its order. The solution approximation is to minimize the objective function of the form:

where- approximated surface, k is the number of points in a localized area.

In order to reduce computational costs for future hardware implementation of the proposed algorithm, we shall only consider surfaces of the first order:. The formation of local areas for further approximation is performed so that the detected anomalous values were located in the center. Not exclude cases in the local scope can have two or more values of the impulse noise. In this regard, we use a generalized approximation of a surface on an uneven grid with the exception of approximation points containing impulse noise. In this case, the condition (11) will be rewritten in the following form:

where xijcoordinates of elements of z.

The solution of the objective function (11) is the estimation of the coefficientssurface. To obtain estimates it is necessary to solve the system of equations of the form:

The estimates of the coefficients are substituted in the equation of the plane and the calculated value of the two-dimensional signal z at the points where the detected anomalous values, i.e. replaced by the values of zi, jfor such i,j, where the condition.

The procedure for fixing values of impulse noise in the processed image is repeated for all localized areas relative to the detected values imposing noise.

Device detection and removal of impulsive noise in image processing (figure 4) contains a buffer block 1, the input of which is an information input device and the first output of which is connected to the first input of the block forming the threshold value 3, the first input of the block approximation 13 and to the first inputs of blocks comparison 4.P, the outputs of which are connected to the inputs of buffer blocks 5.P, the outputs are connected to first inputs of blocks detect areas of non-6.P, the outputs of which are connected to I the dam blocks are zeroed 7.P, the outputs are connected to the inputs of the block of the adder 8, the output of which is connected to the input of the block determining the average value of fines 9, the output of which is connected to the first input unit 10 comparison, the output of adder 8 is connected to the second input of the comparison of 10, the output of which is connected to the input of the buffer 11, the output of which is connected to the first input unit localization values of impulse noise 12, the output of which is connected to the second input of the block approximation 13, the output of which is connected to the input of the correct values of the impulse noise 14, the output of which is connected to the input of the buffer block 15, whose output is information the output device; the first output of the control unit 2 is connected to the second input of the block forming the threshold value 3, the outputs of which are connected to the second inputs of blocks comparison 4.P; the second output of the control unit 2 is connected to the second inputs of the blocks of the detection areas of non-6.P, the third output control unit 2 is connected to the second input of the block localization values of impulse noise 12, the fourth output control unit 2 is connected to the third input of the block approximation 13; synchronisation device provides the clock pulses 16.

Device detection and removal of impulsive noise in image processing works as follows. Ex is the initial digital image is passed to the block buffer, where it is stored and transmitted in a set of thresholds, where the determination of the range of values of the digital image and split it into P+1 range by forming the P thresholds, is formed thresholds mkwhere k=1...p Each value thresholds mkand the original digital image is simultaneously received in the P channel processing. In each channel is compared to the threshold value mkwith the value of the original digital image in units of comparison. When exceeding the values of the original digital image threshold value mkthe corresponding pixel receives the penalty is equal to 1, and is recorded in the block buffer. Received penalty values are received at the inputs of the blocks of the detection areas of instability, and checked for the presence of closed, filled with penalty values area D - area digital software detector, which is set in the control unit. Detected areas are areas of nonstationarity and should be set to zero. Labeled arrays of fines received in block zero. In block zero is zero penalty values for the fields defined by the detection unit as non-stationary. With the outputs of the blocks are zeroed fields penalty values of P channel array fines post the try block of the adder, determine the resulting array of fines by the adder of the P channels separately for each element of the array.

The values of the resulting array of penalty values with block adder receives the block determining the average value of fines and the unit of comparison. As a result, the first input of the comparison is fed the value of the average penalty resulting array with unit determining the average value of fines, and on its second input receives the array of penalties. In the Comparer compares the resulting values of the fines with an average value of fines. Thus, the values of impulse noise are those pixels whose amount received penalty values will exceed the average value of the fines. The result of processing of the resulting array of penalties written into the block buffer. Thus, the block buffer contains an array of penalties, in which the coordinates of the nonzero elements correspond to the coordinates of impulse noise in the original digital image.

To correct the values of the impulse noise in the localization block value impulse noise localize the region with respect to the detected pixels by forming a rectangular or square areas, the coordinates of the centers of which correspond to the coordinates of the detected values. The size and shape of areas localise the tion is specified by the control unit. Coordinates of local areas are passed to the block approximation, where each of the areas is the approximation of the values of the original image surface of a given order, the degree of approximation of the surface is defined by the control unit. Approximation at each localized area of the image is carried out taking into account the detected values of the impulse noise excluded value impulse noise in approximating the surface of the given order). The unit address is overwritten with the value impulse noise on the obtained values of the approximating surface. The processed image is passed to the block buffer, the output of which is an information output device. The synchronisation device provides the clock.

This method of detecting and eliminating values of impulse noise is implemented as follows. In block buffer 1 is written to the source digital image. The control unit 2 determines the number of thresholds, the size of the field of digital software detector, the size of the local regions, the degree of the approximating plane. The set of threshold values 3 defines the threshold values, the number of which is specified by the control unit 2. In blocks comparison 4.P compares the threshold value mkwith the values of the original digital and what the considerations applying. If the value is larger than the threshold value mkthen this pixel receives the penalty is equal to 1, which is recorded in the blocks of the buffer 5.P. Penalty values from outputs of block buffers 5.P arrive at the inputs of the blocks of the detection areas of non-6.P, where a check will be processed the array enclosed area D non-zero values of the elements of the array that corresponds to the areas of nonstationarity in the original digital image, where D is the area of digital software detector, which is set in the control unit 2.

In block zeroing 7.P is reset accumulated penalty values for the fields defined by the detection unit areas nonstationarity 6.P. outputs of the blocks are zeroed 7.P penalty values are received in block adder 8, which is determined by the array of penalties. The values of the resulting array of penalties from a block of the adder 8 are received in the block determining the average value of fines 9. As a result, the first input unit of comparison 10 receives the average value of the penalty unit determining the average value of fines 9 and to the second input of the result array penalty value from the block of the adder 8. In block comparison 10 compares the resulting values of the fines with the average value.

Thus, in the original digital depicted and are considered to be impulse noise values of those pixels, whose amount received penalty values will exceed the average value of the fines. The location in the source digital image values of the impulse noise is recorded in the block buffer 11. The coordinates values of the impulse noise received in block localization values of impulse noise 12, the size of the localized region is set by the control unit 2. The coordinates of the localized regions are passed to the block approximation 13, where each of the areas is the approximation of the values of the original image surface, the degree of the approximating surface is determined by the control unit 2. Approximation at each localized area of the image is carried out taking into account the detected values of the impulse noise excluded value impulse noise in approximating the surface of the given order). In block elimination 14 are replaced by the values of the impulse noise on the obtained values of the approximating surface. The processed image is passed to the block buffer 15, the output of which is an information output device. The synchronisation device provides the clock pulses 16.

The technical result is the detection and elimination of values of impulse noise on digital images.

Through simulation it was found that the proposed the first method has the following advantages:

- allows you to detect and correct value impulse noise with limited a priori information about the statistical characteristics of the undistorted image, active additive noise and impulse noise.

1. The method of detection and removal of impulsive noise in image processing is that the values of the source digital image is compared with different threshold values, a set of penalties for values of the original digital image, which exceeded the established thresholds are formed resulting penalty values by summing the values of fines separately for each sample, the samples in which the resulting penalty values exceed the estimated threshold level are considered abnormal, characterized in that the formed two-dimensional arrays of fines for each threshold level, determined by two-dimensional region of nonstationarity and localized two-dimensional software detector, followed by zero penalty value, the detected value impulse noise are eliminated by replacing these values with the fitted surface of the first order in localized areas.

2. Device detection and removal of impulsive noise in image processing contains buffer block 1, the input of which is the information input device, and the first output of which is connected to the first inputs of blocks comparison 4.P, the outputs of which are connected to the inputs of buffer blocks 5.P, the outputs are connected to first inputs of blocks detect areas of non-6.P the outputs are connected to the inputs of the blocks reset 7.P the outputs are connected to the inputs of the block of the adder 8, the output of which is connected to the input of the block determining the average value of fines 9, the output of which is connected to the first input unit 10 comparison, the output of adder 8 is connected to the second input of the comparison, the output of which is connected to the input of the buffer block 11; the output of the buffer block 1 is connected to the first the input processing unit threshold 3, the outputs of which are connected to the second inputs of blocks comparison 4.P, characterized in that the output of the buffer 11 is connected to the first input unit localization values of impulse noise 12, the output of which is connected to the second input of the block approximation 13, the output of which is connected to the input of the correct values of the impulse noise 14, the output of which is connected to the input of the buffer block 15, whose output is an information output device; the first output of the control unit 2 is connected to the second input of the block forming the threshold value 3, the second output control unit 2 is connected to the second inputs of the detection blocks areas of non-6.P, which is the second output of the control unit 2 is connected to the second input of the block localization values of impulse noise 12, the fourth output of the control unit 2 is connected to the third input of the block approximation 13; synchronisation device provides the clock 16.



 

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8 dwg

FIELD: physics; computer engineering.

SUBSTANCE: invention relates to computer engineering and can be used in control and signal processing systems. Technical outcome is achieved due to that, the device contains a unit for storing measurement results, commutators, interval division unit, random number generator, unit for eliminating associated values, ranking unit, storage register for random number samples, approximation units, subtracting units, remainder storage units, units for obtaining an ordered series on intervals, truncated sampling units, units for calculating mean-square deviation, multiplier units, coefficient storage register, coefficient evaluator, unit for setting false alarm probability, comparators, penalty storage units, arithmetic adder, threshold evaluator, comparator, penalty storage register, unit for eliminating anomalous measurements, storage register, delay unit and a clock pulse generator.

EFFECT: detection and elimination of anomalous measurements with a fixed value of false alarm probability.

1 dwg

FIELD: computer engineering.

SUBSTANCE: invention relates to computer engineering and can be used in systems for controlling and processing multi-dimensional signals. The device comprises a unit for storing input realisation 1, unit for calculating first coefficient 2, unit for calculating second coefficient 3, unit for calculating third coefficient 4, approximation unit 5, unit for evaluating useful component 6 and clock generator 7. Approximation of values of initial discrete realisation of the measured process is done through minimisation of the objective function, which is a sum of mean square deviations of two-dimensional evaluation of useful component from the initial two-dimensional realisation of measurement results.

EFFECT: separate two-dimensional evaluation of useful component given a single realisation of the measured process.

1 dwg

FIELD: computer engineering.

SUBSTANCE: invention relates to digital computer engineering and can be used in digital signal processing systems for optimum nonlinear filtering. The device contains six units for generating matrix functions, three correctors, three units for generating difference, three units for generating sum, three delay lines, unit for generating and outputting prior data, and two units for calculating regularisation parametre.

EFFECT: increased accuracy of evaluating information process parametres in measuring systems.

8 dwg

FIELD: information technology.

SUBSTANCE: proposed invention relates to information measuring devices and can be used in computer engineering, in signal control and processing systems. The devices has a register for storing measurement results (1), delay unit (2), approximation unit (3), register for storing estimations (4), averaging unit (5), control unit (6), clock generator (7), and a comparator unit (8).

EFFECT: pickup of useful signal against a background of noise with minimisation of end effects, in conditions of insufficient prior information on statistical characteristics of adaptive noise and useful signal function given a single realisation of the measuring process.

4 dwg

FIELD: information technologies.

SUBSTANCE: device comprises unit of input realization storage, clock oscillator, control unit, unit of useful signal extraction, unit of storage of five last values of useful component assessment, unit of approximation with polynom of the first degree, unit of approximation with polynom of the second degree, unit of output realization storage. In device end values of assessment are approximated with the help of method of least squares with polynom of the first or second degree, then produced equation of assessment is used to calculate values in forecast points.

EFFECT: forecasting measurement results on the basis of useful signal extraction without end effects, under conditions of limited a priori information about useful and accidental component.

1 dwg

FIELD: information technologies.

SUBSTANCE: device comprises serially connected frequency filter, digitiser and unit of reduction to perfect instrument (RPI), intended for interpolation of counts supplied to its inlet, detection of weight of basic final duration of signals in inlet signal on the basis of interpolated counts decomposition into Fourier series by orthogonalised reactions of frequency filter into basic signals and for formation of outlet signal as a superposition of basic signals with account of their weight in inlet signal, besides versions of device include connection of noise suppression unit or serialy connected unit of signal growth speed assessment and normalisation unit between digitiser and RPI unit.

EFFECT: improved resolution and sensitivity to elements of signal, increased efficiency and simplification of device for signals processing.

4 cl, 12 dwg

FIELD: information technology.

SUBSTANCE: device has a unit for storing input realisation, switches, approximation units, estimation storage units, arithmetic adder, a unit for storing useful component estimates, a control unit, a delay unit, a clock-pulse generator, two units for breaking down into intervals, each having a random number generator, a unit for averaging related values, a ranging unit and a register for storing random number samples. The control unit has a shift register for sampling column random numbers, a shift register for sampling row random numbers, a delay unit for sampling column random numbers, a delay unit for sampling row random numbers, a counter and a unit for checking conditions.

EFFECT: two dimensional estimation of the useful component in conditions with insufficient prior information on statistical characteristics of additive noise and useful component function.

2 dwg

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