# Image reconstruction device

FIELD: information technology.

SUBSTANCE: device has an image storage unit, a pixel storage unit, a turning unit, a vocabulary forming unit, a vocabulary storage unit, a processing unit, a priority calculating unit, turning unit, an adaptive form determination unit, a similarity search unit, a pixel averaging unit, an image filling unit and a clock pulse generator.

EFFECT: reconstruction of image pixel values under incomplete a priori information conditions.

6 dwg

The invention relates to the field of computer engineering and can be used in digital television and photosystems, global positioning systems and surveillance.

A simplified mathematical model of the image represents a two-dimensional discrete signal(figure 1), where S_{i,j}- the available pixels in the undistorted image, η_{i,j}- the area of the image with missing pixels, δS - border region S.

The main task - the restoration of the values of the pixels of the image.

Reconstruction and retouching of images involves the removal of scratches, stains, dust, unnecessary labels, objects and other defects from the surface of photos and restore the missing fragments with the use of accessible areas of the image. When processing an archive of images, such as images of Museum documents or photos, the challenge is to remove various defects (spots, lines, folds, and other damaged areas and restore damaged areas without disturbing the structure of the image. In the video found a static image, which impede the view, closing of the useful information from the viewer. Such images include different channel logos, date, time, or subtitles, which were imposed on the film further to what derounian. Private class fields that interfere with viewing the video are distorted blocks when the codec, the appearance of which is explained by the unreliability of the data transmission from the encoder to the decoder.

Simplified methods of reconstruction of the values of the pixels of the image can be divided into the following groups:

1) Methods based on solutions of differential equations.

2) Methods based on orthogonal transformations.

3) Methods based texture synthesis.

Analysis of existing methods of treatment shows that the area of their use in a limited amount of information about the components of the processed process is extremely limited. Using the methods of reconstruction of the values of the pixels of the image based on the solutions of differential equations in partial derivatives leads to the blurring of sharp drops in brightness and contours and requires a priori information to select methods and minimization of the functional. Failure to restore the texture images and curved paths limits the scope of use of these methods, which are mainly applicable when removing scratches and small defects on the structure of images. To use methods based on orthogonal transformations require a priori information to select the threshold is about values, orthogonal basis and the size of the blocks of the spectral representation. It should also be noted that these methods lead to blurry textures and patterns when restoring large areas with missing pixels, and a large number of iterations leads to a significant computational cost. The application of methods based texture synthesis requires a priori information about the size and shape of the restoration and the geometrical properties of the images to select options of ways.

Known digital smoothing device with advanced detection and elimination of anomalous dimensions [Patent No. 2010325, IPC G06F 15/353]. This device can be used when processing images, and the lost pixels are abnormal. The block detection and elimination of abnormal measurements provides obtaining the absolute value of the differencebetween the current k-th reference input signal S_{k}and the value of the smoothed output signalthe comparison circuit provides a comparison signal of the absolute value of the differencecode valid values of Δ and gate generates an output characteristic is exceeded.

In the smoothing device is implemented following the smoothing algorithm:

the de values of m_{
k}and m_{k-1}defined as the present value of the input signal relative to its average value, respectively, for the k-th and (k-1)-th moments of the current time, which is equal to:

The value of Δ is valid strobe shows the deviation of the input signal, N_{C}- the value of the division factor.

When checking conditionswhich is the absence of errors, the transition occurs on the branch of computing. If no errors are not met, then the calculated value of m_{k}it is considered wrong and instead for the formation of the current smoothed value is the previous correct value of m_{k-1}. Such a replacement due to the monotonicity of the original smoothed signal does not lead to its distortion. If after this the next step of smoothing the absence occurs, the error is classified as a fixed anomalous dimension. Failure to comply with the conditions of absence of errors is a sign of failure.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: storing digital signal, the comparison with the threshold level, the recovery of abnormal values.

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

- %the fool detection ineffective in the treatment group of abnormal values;

- requires a priori knowledge of the possible values of Δ gate.

Barriers to achieving the desired technical result are as follows:

because this method allows to detect only a single abnormal value, the detection efficiency is a group of abnormal values will be low;

- the value of Δ is set depending on the class of input signals and their applications.

The structural scheme of the device that implements the algorithm, contains the first adder, the counter counts, the first and second decoders, the first and the second element And the element OR trigger block set division ratio of the first register and the second adder, the second register, the third decoder, the counter anomalous dimensions, the unit selection module, the comparison circuit, the third element and the clock.

The known method and device for filling object based rasterized image (Filling of graphical regions) [USA Patent No. 08/053, 212)]. The method is based on the rasterization of images and includes analysis on the intersection of different objects each pixel along a raster line the edges of the field of recovery. Priority pixels for recovery are determined based on the selected level data and one of the many different objects with a high level of priority to the spine.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: storing digital signal, restoring the lost pixel values.

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

- priori information about the structure of the image and the size of the field of recovery for the choice of parameters of the method.

A known method of restoring images on the basis of the solution of differential equations [M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, Image inpainting // Computer Graphics Proceedings, K.Akeley, Ed. ACM Press / ACM SIGGRAPH / Addison Wesley Longman, 2000. - P.417-424]. This method allows you to connect the contours of constant brightness of the image across the field recovery using solutions of differential equations by minimizing a chosen functionality.

The direction of the lines is set by using the boundary conditions at the edge of the field of recovery, which is defined by the expression:

Differential equation in partial derivatives has a solution, provided that:

This expression determines the direction of continuation lines using the smoothing operator ΔS in the area of recovery. Anisotropic diffusion is calculated iteratively for all pixels using the expression:

where k(i, j, t) is the curvature two-dimensional plane S(i, j, t) at the point (i, j).

The boundary conditions for the recovery image is to match the intensity of the brightness values of the image at the boundary area reduction, as well as the direction of the lines of the contours.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: storing digital signal, calculation of the gradient, the recovery of lost pixel values.

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

- smoothing the sharp luminance transitions images, which significantly degrades the visual quality of the reconstructed image.

Known nonlinear method based on adaptive discharged representation of signals through the principles of nonlinear approximation [O.G. Guleryuz Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising // Part I: theory, IEEE transactions on image processing, 2006. - V.15. - No.3]. Adaptive determined by the index set of spectral coefficients, which predicts the missing area in the image. Orthogonal transformation to the image will be written in the form:

D=GS,

where D is the spectral coefficients, G is the matrix of an orthogonal transformation image.

However, you can write that G=[G_{I}G_{J}]where I denotes insignificant the factors in the transformation,
a J - significant. Further insignificant coefficients are equal to zero:

When G_{I}can be represented in the form of two components - available and missing pixel values:

Then condition (2) can be written in the form:

The solution is determined by iteration and has the form:

where R is a constant, D is the matrix of significant coefficients, k is the iteration number.

As the zeroth iteration is used to fill the squarerandom numbers.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: storing digital signal, restoring the lost pixel values.

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

- a priori information to select the parameters of the method, for example, the number of levels, which split the region with missing pixels, the overlap factor levels of decomposition, the size of the blocks of the spectral representation and the threshold level to determine significant factors;

- restoration of the pixel values is equal for all aspects of recovery, which sometimes leads to the fact that the structure of the image with the contours and drop the mi brightness is restored correctly in the center of the region;

the use of this method leads to blurry textures and patterns when restoring large areas with missing pixels, and the number of iterations can reach about 500, which leads to significant computational cost.

Known way to restore texture and structure image [M. Bertalmio, L. Vese, G. Sapiro, and S. Osher Simultaneous texture and structure image inpainting // Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2003. - P.707-712.], which allows to extrapolate the pixel values of the images as in the structure and texture images, each component appears to be discharged significant coefficients of the spectral conversion. This method is based on decomposition of the image, also called morphological component analysis, divides the image into a linear combination of textures and patterns of the image. To restore the texture is curved transformation and patterns - the discrete cosine transform.

The image is represented as the sum of:

where G is the matrix of an orthogonal transformation image, D is the matrix of significant coefficients, s is the structure of the image, t is the texture of the image.

While the expression of the objective function to determine the significant factors will be written in the form:

where M is a mask region with missing pixels, γ, λ - parameters of the method, TV - total variation (adjustment using the model the total variance), R is the remainder, which in the first iteration is equal to the random number.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: storing digital signal, restoring the lost pixel values.

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

- priori information about the structure of the image, and restore to select the parameters of the method;

- restoration of large areas using a known method leads to blurring of the image patterns, and a large number of iterations considerably complicates the computational cost.

A known method of restoring images on the basis of filling in similar areas and the device for its realization (Image region filling by exemplar-based inpainting) [USA Patent No. 11/095,138, No. 10/453,404].

The first step calculates the priority P(p) for each pixel boundaries, which consists of two factors:

where p is the current pixel on the boundary of the available pixels.

(P) - data trust;

D(p) is the gradient data;

the number of pixels kVA the military unit with the center pixel p;

the vector orthogonal to the gradient at the point p;

n_{p}the vector orthogonal to the boundary of ∆ s at the point p;

α - normalized multiplier, for black-and-white images is 255.

First, it is assumed that the data value of the trust for the pixels of the area S is equal to 1, and for the0.

The calculation of priority by using the expression (1) allows us to give more weight to the pixels on the changes in brightness (borders), thus restoring them first. Accounting data trust(R) allows you to assign less weight to the restored pixels with increasing distance from the available pixels of the region S.

The second step is the block ψ_{q}in the area of available pixels S, for which the Euclidean norm minimum:

The pixel values of the identified block is copied into the. Data trust for the restored pixels are assigned equal to the current value of C(R). The translation procedure priority and search for similar areas with the subsequent replacement of repeats.

Signs of a device similar to the matching characteristics of the proposed technical solution, the following: storing digital signal, calculation of coefficient of priority, search for similar blocks, restoring otherany pixel values.

The disadvantages of the known devices are:

- visibility of borders in the restored image was found between similar units;

- improper restoration, in the absence of a similar block;

- the dependence of the efficiency of the recovery from the selection of the block size.

Closest to the invention is a device for processing a two-dimensional signals in the reconstruction of images [Application No. 2010132434, IPC G06F 17/17]. The considered device-prototype assumes:

1) records the values of the input image

2) is determined by the value of the coefficient of trust With,ifif;

3) calculates the priority value P (∆s_{i,j}for each value of a pixel boundary P (∆s_{i,j}) =(∆S_{i,j})·D (∆s_{i,j}), where

4) is determined from pixel p∈(i,j) with the maximum priority value max(P(δS_{i,j})) on the boundary δS;

5) is determined by the square shape of the area to search for similarity Ψ_{p}with the Central pixel p∈(i,j);

6) calculates the Euclidean metric for all available values of the pixels of the image,;

7) number of blocks of similarity R is determined using the confidence interval:

where:; α is the significance level;

8) the pixel values in the region η adjacent to the pixel with the highest priority p, is recovered by averaging corresponding pixels found areasfrom the area of available pixels S_{i,j}:

9) the coefficient of trust for the restored pixel is assigned equal to the current value of C(R). Then, the translation procedure priority and search for similar areas with the subsequent replacement of repeats.

Device for the recovery image contains the block storage image storage unit pixels, the block of the dictionary storage unit of the dictionary, the search block similarity, the processing unit, the computing unit priority definition block adaptive forms, block averaging of pixels, the block filling image.

The disadvantages of the known devices of the prototype are;

- improper restoration, in the absence of a similar block;

- the dependence of the efficiency of the recovery from the structure of the image.

Barriers to achieving the desired technical result are as follows:

- the lack of a similar block leads to improper restoration, since the replacement pixel is the pixel block for which the Euclidean metric mi is emalina, even if it is of great importance in absolute value;

- on the image when searching for similar blocks is not taken into account their orientation.

The proposed device for image reconstruction can reduce the error recovery images by increasing the number of similar blocks by rotating them. The device implements the following algorithm. The first step calculates the priority value P (∆s_{i,j}for each pixel value of the boundary, which consists of two factors (figure 2):

where δS_{i,j}- the current pixel on the boundary of the available pixels; (δS_{i,j}) - coefficient of the trust; D(δS_{i,j}- the coefficient of the gradient; ΨδS_{i,j}- a square block of pixels centered at pixel δS_{i,j};the number of pixels of a square block,

the vector orthogonal to the gradient at the point ∆ s_{i,j}; n_{δSi,j}the vector orthogonal to the boundary δS at the point ∆ s_{i,j}; α is the normalized multiplier for eight-bit images is 255.

First, it is assumed that the coefficient of trust With the pixels from the area ofequal to 1 for the region of η is 0.

The calculation of priority by using the expression (3) allows us to give pain is the second weight to the pixels,
which are the differences of brightness (borders), thus restoring them first. The factor of trust(δS_{i,j}allows you to assign less weight to the restored pixels with increasing distance from the available pixels from the.

In the second step, for a pixel p∈(i,j) with the maximum priority value max(P(δS_{i,j})) on the boundary δS using the method of inversion adaptive determined by the shape of the area to search for similarity, which allows to correctly take into consideration the shape of the area of recovery and not to capture excess boundaries, which can lead to an incorrect reconstruction of the image.

For the formation of adaptive areas of two-dimensional signal for a pixel p∈(i,j) are the eight directionsdetermining intervals of quasi-stationarity. The condition of quasi-stationarity is tested by calculating the random variable τ is equal to the sum of the number of inversions of the values of the pixels in each of the areas of two-dimensional signalwhere are the available pixels.

For example, the sum of the number of inversions for direction 5 is equal to:

where S_{i+l,j}l=0...d-1 to the current value of the image pixel with coordinates (i+l,j); S_{i+k,j}k=l+1...d - th is blowing the values of pixels in the j-th column (in the direction of 5),
R - the maximum length of the quasi-stationarity interval.

The number of combinations, for which we compute the sum of the inverses is:

The first alternative (decreasing signal) is received, if

The rule for the adoption of the second alternative (increased signal) is

where α is the error value of the first kind.

The hypothesis of stationarity of the signal, if

Received the boundaries of the intervals for each of the eight sectors formed by lines 1-2, 2-3, 3-4, 5-6, 7-8, 8-1, is the formation of regions of quasi-stationarity. For this, we use linear interpolation of the boundaries of adjacent intervals by the equation of a line passing through two points:

,

where (i_{1}I ,j_{1}) - coordinates of the boundary direction h, (i_{2}I ,j_{2}) - coordinates of the border areas h+1.

Pixel values that fall between all the directions and the interpolating straight lines passing through the boundaries of the intervals of quasi-stationarity, are combined into one region Ω.

For a pixel adjacent to the pixel p∈(i,j)having a larger valuealso defined adaptive region using the method of inversion. Each is th of the received fields is quasi-stationary,
and they are on opposite sides of the differential brightness. These areas are combined into one (figure 3), thus, defines the scope Ψ_{p}with adaptive size and brightness difference.

Defines the pixel p∈(i,j) with the maximum priority value max(P(δS_{i,j})) on the boundary δS and selected adaptive region Ψ_{p}belonging to a given pixel, which allows you to correctly take into consideration the shape of the area of recovery and not to capture excess boundaries, which can lead to an incorrect reconstruction of the image. Further, the number of blocks obtained from the original image with the available pixels is increased by rotating them by 90, 180, 270 degrees. This approach allows to reduce the error recovery image by increasing the number of units and increase the likelihood of finding the most similar block in the Euclidean metric (figure 4)

In the third step are blocks,in the area of available pixels S_{i,j}for which the Euclidean metric minimum (figure 5):

,

when h denotes the sequence number of such blocks, ranked by the Euclidean metric.

The number of blocks of similarity R is determined using the confidence interval:

; α is the significance level.

The pixel values in the region η adjacent to the pixel with the highest priority p, is recovered by averaging corresponding pixels found areasfrom the area of available pixels S_{i,j}:

The coefficient of trust for the restored pixel is assigned equal to the current value of C(R). Then, the translation procedure priority and search for similar areas with the subsequent replacement of repeats.

Device for image reconstruction (6) contains the block storage image 1, the first input by the information input device, the second output of which is connected to the input of the storage unit pixels 2, the output of which is connected to the input of block rotation 3, the output of which is connected to the input of the block of the dictionary 4, the output of which is connected to the input of the storage unit of the dictionary 5, the output of which is connected to the second input of the search block similarity 9; the third output of the storage unit of the image 1 is connected to the input of the processing unit 6, the output of which is connected to the input of the computing unit priority 7, the output of which is connected to the input of unit definition adaptive forms 8, the output of which is connected to the first input search block similarity 9, the output of which is connected to the input of block averaging of the pixels 10, the output of which is under the offline to the entrance of the block of image filling 11, the output of which is connected to the second input of the storage unit of the image 1, the first output by the information output device; a timing device is provided by a clock 12.

Device for image reconstruction is implemented as follows. To the input of the storage unit of the image receives the image with missing pixels. The available pixels are stored in the storage unit pixels. In the block of the dictionary created a two-dimensional matrix, taking into account the rotation of the image before you create the dictionary. These matrices are used to fill areas of the image with missing pixels. Filling occurs for pixels adjacent to the pixel for which priority is maximum. The processing is done iteratively until all pixels in the block of storage of the image will not be restored, then the received value of the received information to the output device.

Device for image reconstruction works in the following way. To the input of the storage unit picture 1 picture of lost pixels. The available pixels are stored in the storage unit pixels 2, then they are rotated by 90, 180, 270 degrees in the block turning 3 and is fed to the input of block creation of the dictionary 4. The result of the formation of the dictionary stores the I in the storage unit of the dictionary 5, the obtained two-dimensional matrix are used later to restore the image. Matrices are created by forming square blocks of size 15 by 15 pixels from the original image by shifting the block for all of the available pixels of the image. In the processing unit 6 is formed of boundary pixels around the area with missing pixels from the storage unit the image 1. Further information on boundary pixels to the input of the computing unit priority 7 that calculates a priority for each boundary pixel, which consists of two factors: the factor of trust and the coefficient of the gradient. This block also is the ranking of priority and defining a boundary of the pixel with the maximum priority value. In the block definition adaptive form 8 around the pixel with the maximum priority value is generated adaptive region close to the brightness of the pixels using the method of inversion. Adaptive region to the input of the search block similarity 9, in which the calculation of the Euclidean metric with all two-dimensional matrices, which are stored in the storage unit dictionary 5. In the search block similarity 9 also defines a number of similar blocks, for which the Euclidean metric does not exceed the threshold value. The data blocks are sent to the input of block averaging the peak of the oil 10, in which there is a formation averaged estimates. This estimate is passed to the block fill image 11, which copies the values of pixels adjacent to the pixel with the highest priority, the average assessment in the storage unit of the image 1 to the appropriate coordinates. Next, the calculation process priority, search for similar blocks and subsequent replacement is repeated until then, until you have recovered all the values in the storage unit of the image 1. Synchronous operation of the device is provided by a clock 12.

The technical result - the reconstruction of the values of the pixels of the image under conditions of incomplete a priori information.

A device for the restoration of images that contains the block storage image, the first input by the information input device, the second output of which is connected to the input of the storage unit pixel; an output block of the dictionary is connected to the input of the storage unit of the dictionary, the output of which is connected to the second input of the search block similarity; the third output of the storage unit of the image connected to the input of the processing unit, the output of which is connected to the input of the computing unit priority, the output of which is connected to the input of block definitions adaptive forms, the output of which is connected to the first input of block similarity search, the output of which connection is Chen to the input of block averaging of pixels, the output of which is connected to the input of block filling image, the output of which is connected to the second input of the storage unit the image, the first output of which is an information output device, wherein the output of the storage unit pixels connected to the input of block rotation, the output of which is connected to the input of block creation of the dictionary, the synchronisation device is provided with clock pulses.

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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

FIELD: physics.

SUBSTANCE: function codes are rounded-off to the nearest level and the obtained codes are stored. The optimality criterion code is calculated and stored. Starting with a certain initial number L of the function code, the direction of rounding-off this code is changed and the optimality criterion code is calculated. If the optimality criterion code falls, the changed value of the code is stored and a new value of the optimality criterion code is calculated and stored, otherwise the initial L-th function code and the initial optimality criterion code are stored, and calculation is moved on to the next number of the function code L+1, where it is checked whether the optimality criterion code falls in the same way as that when the L-th function code was changed. Further, the process is continued until the optimality criterion code does not fall in a sequence of n function codes, read from the code value in which the last fall in the optimality criterion code took place.

EFFECT: reduced absolute error in the amplitude of the reproduced sinusoidal signal.