Method and apparatus for detecting defects on video signals

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to computer engineering and can be used in video sequence analysis and processing systems and digital television. The method of detecting defects on video signals includes analysing differential images of adjacent frames. Binarisation, expansion and joining operations are then applied to the differential images of adjacent frames. Non-zero values are analysed on the obtained arrays, for which a defectiveness decision is made on the initial frames based on dispersion of the initial values.

EFFECT: detecting the position of defects on video signals with insufficient prior information about statistical characteristics of additive noise and useful component function.

2 cl, 1 dwg

 

The present invention relates to the field of computer engineering and can be used in control systems and processing of multidimensional signals.

A simplified mathematical model of the dynamic digital image that represents a two-dimensional discrete sequenceYi,j(k),i=1,N,j=1,M,k=1,Kthe form:

Yi,j(k)={Si,j(k),ηi,j(k)={}ηi,j(k),/mo> ηi,j(k){},i=1,N,j=1,M,k=1,K,(1)

whereSi,j(k)useful two-dimensional component (undistorted frame of video),ηi,j(k)- the area with missing or distorted pixels (defects), N is the number of rows, M is the number of columns of the two-dimensional array image, K is the number of frames of the two-dimensional array of dynamic images. It is assumed that framesY i,j(k-1)andYi,j(k+1)correlated with the Central frameYi,j(k). Defective regionηi,j(k)presented in the form of distorted values of the pixels, the time of observation which, as a rule, no more than 1-2 frames in the sequence.

The main task is the detection of the position of the defects on the video.

A similar problem can occur: (1) automatic recovery of archival recordings; 2) systems of digital processing of sequences of correlated images; 3) radio systems for processing multidimensional signals.

With the development of digital video recording systems on the background fade system analog video. Unfortunately, the features of the analog video tapes is such that over time the material for icandy on it partially distorted. This is due to poor equipment, improper storage of video tapes, as well as its demagnetization. All these factors lead to the emergence of film defects in the form of a single or group of pixels, the value of which brightness will significantly stand out from the rest of the pixels. The time of observation of these defects, usually not more than 1-2 frames in the video sequence. The recovery of such defects at the present time in most cases is carried out by manual frame-by-frame processing, which complicates the recovery of archival videos, old movies and documentaries. Therefore it is an urgent task of the automated restoration of archival recordings.

The traditional approach to the problem of removal of defects can be divided into two stages:

1. Detection of defects (getting the mask to the position of the defective pixels).

2. Remove defects (recovery frames of the video sequence).

There is a method of ranking pixels (ROD), the heuristic detector based on rank statistics [A. Gangal, So Kayikcioglu, and C. Dizdaroglu, "An improved motion-compensated restoration method for damaged or color motion picture film," Signal Proc: Image Communication, vol. 19, pp.353-368, 2004]. Let prwhere r=1, 2, ..., 6 establishes communication between adjacent pixels inYi j(k). These pixels are taken from considerations of the motion compensation on the previous and subsequent frames, in places spatially close to the pixel inYi,j(k)and its two nearest vertical neighbors. Let dmthe sequence of pixels prsorted by rank d1≤d2≤...≤d6. Here dmean- the average value of the sequence and rank-ordinal differences ROD(ηi,j, l), where l=1, 2, 3...:

dmean=d3+d42,

ROD(ηi,j,l)={d1-Yi,j(k)(ηi,j)ifYi,j (k)(ηi,j)dmeanYi,j(k)(ηi,j)-d7-lifYi,j(k)(ηi,j)>dmeanwithl=1,2,3

The defect is detected if at least one of the ranked sequence exceeds the threshold Tl. The threshold T1is specified by the user, and determines the sensitivity of the detector [Nadenau M. J. and Mitra S. K., "Blotch and scratch detection in image sequences based on rank ordered differences," Time-Varying Image Processing and Moving Object Recognition, Elsevier, pp.27-35, 1997.]:

LROD(ηi,j)={1i fROD(ηi,j,l)>T0elsewith0T1T2T3andl=1,2,3.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: allocation of the sequence of frames from the original video sequence, the threshold processing, the receiving mask with defects.

The disadvantages include fixation sensitivity and the choice of the threshold detector, which is set by the user.

Barriers to achieving the desired technical result are as follows:

- the choice of the threshold depends on a priori information about the size and shape of the restoration and the geometric properties of the image.

Known simplified way of ranking pixels ROD, detector (SROD) [van Roosmalen, R. M. C., J. Biemond, and R. L. Lagendijk, "Restoration and storage of film and video archive material," Signal Processing for Multimedia, 1999.]. The difference of the ROD detector from the SROD is that of the area in monogo detector is not the median value, and the difference between the local maximum and minimum. Denote sorted in ascending order of the brightness values of the set of pixels for the n-th frame through the pri∈[1,6]. Then the decision rule can be written as the following expression:

SROD(i)={min(pr)-Yi,j(k)(ηi,j)ifmin(pr)-Yi,j(k)(ηi,j)>0Yi,j(k)(ηi,j)-max(pr)ifYi,j(k)(ηi,j )-max(pr>0)withr=1,...,60otherwise.

The defect is detected, provided that:

LSROD(ηi,j)={1ifSROD(ηi,j)>T10otherwisewithT10.

The SROD detector evaluates the intensity range of pixels obtained by motion compensation, and compares the intensity range of pixels with the condition. The defect is detected if the current pixel intensity is sufficiently distant the outside grade.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: allocation of the sequence of frames from the original video sequence, the threshold processing, the receiving mask with defects.

The disadvantages of the method include the fact that for small values of the threshold values of the defective pixels are detected correctly, but there will be many false positives. When increasing the threshold, the number of detected defects is reduced by reducing the number of false positives.

Barriers to achieving the desired technical result are as follows:

- use coding system does not allow you to post the density distribution of the distorted and undistorted pixels.

A known method of detecting the time lag (SDIa) [Kokaram A. S., "Motion Picture Restoration", Springer Verlag, 1998.]. This detector based on rank statistics. The peak detection index (SDIa) is the most simple heuristic method for the detection of temporal gaps in the sequence. This detector compares the brightness value of each pixel in the current frameYi,j(k)the value is the second brightness to the previous and subsequent frames of a video sequence. Then, it calculates the minimum quadratic difference.

.

Large values indicate a break in the intensity of the image in both forward and reverse temporal direction. The defect is detected if dSDIawill exceed a specified threshold G.

,

where T1, - priori defined threshold.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: selection of frames from the original video sequence, the computation of the dispersion, obtaining a mask with defects.

The drawbacks of a priori defined threshold value, which determines the sensitivity of the detector, which will lead to a large percentage of false positives. Increasing the threshold T1will reduce the sensitivity of the detector, that

will result in the reduction of false detektirovanii, and to reduce the number of true detections.

A known method of restoring a video sequence [patent WO 9937087]. The method can be divided into two stages. At the first stage calculates the motion vectors for large blocks in the image, to remove flicker and stabilize the brightness. This operation is based on phase correlation.

Motion estimation based on done the research institutes of the two-dimensional fast Fourier transform (FFT), and the correlation is generated from the previous field. The correlation data is subject to the inverse 2D-BnO up to a maximum observations and interpolation to obtain one or more motion vectors for each block. Then is the variance of the blocks received, the obtained average value is output.

In the second stage to detect scratches are determined by the horizontal gaps with high vertical and temporal correlation. Scratches are found, if the vertical gaps average horizontal correlation and large temporal correlation. Assessment of the correlation is based on performing two-dimensional fast Fourier transform (FFT) for the current frame.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following:

- allocation of frames from the original video sequence, the detection of temporal gaps, creating a mask with defects.

The disadvantages of the method include large computational costs and the low value of the probability of correct detection for the detection of defects.

Closest to the invention is a method and apparatus for automatic digital restoration of film and video sequences [US 7769244 B2]. In the first step of RGB triples allocates one color component, after which you alauda preliminary mask, obtained by subtracting neighboring frames. The second step is determined by the coherence map for at least one of the color components in four directions, to differentiate smooth texture and texture with gray scales using the following expressions:

F=Y(x0+k, y0), F1,k=Y(x0+k; y0+k), F2k=Y(x0; y0+for k,

P(M0,θj)=(k=-luliluliFj,k)2(2luli+1)k=-luliluliF2, j=0,1,2,3

This operation is necessary in order to further median filtering took place only in the smooth region and not made of distortion in area with changes in brightness. In the third step to smooth areas, apply median filtering with a given a priori by the area of the defect.The fourth step by using morphological operations of opening and closing removes any alleged defects small square:

(Yi,j(k))2=(Yi,j(k)B)ΘB),

(Yi,j(k))2=(Yi,j(k)ΘB)B),

whereYi,j(k)image in shades of gray, B is a structural element.

The characteristics of the prototype method, coinciding with the characteristics of the proposed technical solution, the following: selection of frames from the original video sequence, the dierence between adjacent frames, creating a mask with defects, deletion of objects.

The disadvantages of the known devices of the prototype are:

- a priori-defined area of the effect;

- inability to detect artifacts of the big sizes, as well as semi-transparent stains.

The structural scheme of the device of the prototype contains a block allocation of three frames, the block calculating the difference between frames, the computing unit card coherence, the separation unit smooth textures and borders, block median filtering, block correction of blurred boundaries.

The proposed method for the detection of defects on the video allows you to automatically detect defects on a video sequence in terms of a limited amount of a priori information.

The first step is the recording of the values of the input implement in arraysYi,j(k-1),Yi,j(k),Yi,j(k+1)from the original video sequence, whereYi,j(k-1) andYi,j(k+1)- adjacent frames relative to the frameYi,j(k).

Next, it calculates the difference between framesCi,j(k)=Yi,j(k+1)-Yi,j(k),i=1,N,j=1,MandCi,j(k+1)=Yi,j(k+2)-Yi,j (k+1),i=1,N,j=1,MwhereCi,j(k)- the array obtained after the difference between frames. This operation allows to determine the difference between adjacent frames.

As a dynamic image, inertial, then the scene change and motion for pairs of adjacent frames will vary slightly. The defects of the video signal in the form of groups of pixels, at the level of white and black, are in most cases only one frame. Differential arraysCi,j(k)andCi,j(k+1)in addition to defects also contain the plume from the motion of objects. For selected what I defects arrays Ci,j(k)andCi,j(k+1)binarized [Van Roosmalen, J. Biemond, and R. L. Lagendijk, "Restoration and storage of film and video archive material," Signal Processing for Multimedia. - 1999.]. Then from the resulting arrays are removed from all the groups of pixels, covering an area less than the threshold value [Marchuk C. I. Voronin, C. C., R. (in Russian.. And Automated detection of defective pixels on archival videos. Service in Russia and abroad. - Volume 2 (21). - 2011. Journal. - 04200700021/0016].

Area arrayCi,j(k)andCi,j(k+1)grow a specified number of pixels. Each isolated area in arraysCi,j(k)andC i,j(k+1)is assigned a sequence number.

In the second step for the received arrays applies the exclusive or operation:

Di,j(k)=Ci,j(k)Ci,j(k+1).

The result of this operation is a new array in which the individual elements are assigned only for those areas that do not have intersections for related areas of interest on two adjacent differential framesCi,j(k)andCi,j(k+1).

In the operation of the proposed algorithm is formed an array ofDi.j(k)/msubsup> where as single values are marked defective pixels forYi,j(k+1)frame of the sequence. After the execution of the algorithm the first and second steps are repeated for subsequent frames.

Device defect detection on the video signal contains (Fig.1) block storage sequence 1, the first input by the information input device, the output of which is connected to the input of the power conversion RGB to grayscale 2, the output of which is connected to the input of the storage unit of the first frame 3.1, the first output of which is connected to the first input of the first unit calculating the difference 4.1; the second output of the storage unit of the first frame 3.1 is connected to the input of the first delay unit 3.2, the output of which is connected to the input of the storage unit of the second frame 3.3, the first output of which is connected to the second input of the first unit calculating the difference 4.1; the second output of the storage unit of the second frame 3.3 connected to the first input of the second unit calculating the difference 4.2; the third output of the storage unit of the second frame 3.3 connected to the input of the second delay unit 3.4, the output of which is connected to the input of the storage unit of the third frame 3.5, the output of which is connected to the second input of the second unit calculating the difference 4.2, the output of which is connected to the input of the second block binarization 5.2, the output of which is connected to the input of the second block delete objects in a small area 6.2, the output of which is connected to the input of the second storage unit 7.2, the first output of which is connected to the input of the second computing unit variance 8.2, the second output of the second storage unit 7.2 connected to the first input of the second unit 9.2 comparison, the output of the second computing unit variance 8.2 is connected to the second input of the second block comparison 9.2; the output of the first unit calculating the difference 4.1 connected to the input of the first block binarization 5.1, the output of which is connected to the input of the first block delete objects in a small area 6.1, the output of which is connected to the input of the first storage unit 7.1, the first output of which is connected to the input of the first computing unit variance 8.1, the second output of the first storage unit 7.1 is connected to the first input of the first unit 9.1 comparison, the output of the first computing unit variance 8.1 is connected to the second input of the first unit 9.1 comparison; to the third input of the first and second unit of comparison is connected to the output of the threshold value of 10; the output of the first block comparison 9.1 connected to the input of the first block region-growing, 11.1, the output of which is connected to the first input of the exclusive-or 12; the output of the second Comparer 9.2 connected to the input of the second block RA is rasteniya areas 11.2, the output of which is connected to the second input of the exclusive-or 12, the output of which is connected to the input of the storage unit mask with the detected defects 13, the output of which is an information output device; a timing device is provided by clock 14.

Device defect detection on the video signals is as follows. Frames of the video signal sequentially received from the output of the storage unit of the video 1 input block conversion RGB to grayscale 2, which converts a color of the frame to grayscale, then recorded three successive frames of the video sequence in the storage units of the first frame 3.1, the second frame 3.3, a third frame 3.5 using blocks delay 3.2 and 3.3. In blocks 4.1 and 4.2 are written to the calculated differential image between frames of the blocks 3.1 and 3.3, 3.3, and 3.5, respectively. In blocks 5.1 and 5.2 of the obtained differential image binarized, which enables to obtain a preliminary mask with defects. Next, in blocks 6.1 and 6.2, there is an analysis of the preliminary mask defects, and removes all the objects in a small area of less than 0.7% of the size of the original frame, this operation allows you to delete some of the false positives associated with the appearance of small defects from train movements. The block is x 7.1 and 7.2 are stored mask with the preliminary results of defect detection, received at the inputs of blocks 8.1 and 8.2, in which the dispersion is calculated on adjacent frames on a preliminary masks, in those places of frames on the mask detected prior defects. The calculated variance is compared in block 9.1 and 9.2 with a threshold, which is stored in the block 10. In boxes 11.1 and 11.2 above objects of the preliminary mask performs morphological operations expansion and closure. The resulting masks are received at the inputs of the block 12 "exclusive or", in which using the Boolean operations are analyzed two processed differential frame, the result is the resulting mask defects, which is stored in the block 13. Synchronous operation of the device is provided by clock 14.

The technical result is the detection of the position of the defects on the video signals in the conditions of insufficient a priori information about the statistical characteristics of the additive noise and the useful component.

1. Method of detecting defects in a video signal, consisting in the analysis of differential frames of the video sequence, wherein the differential images of adjacent frames apply the binarization operation, expansion and closure, the resulting arrays are analyzed non-zero values for which the source frames decisions taken defects on dispers the original values.

2. Device defect detection on the video signal that contains the block storage sequence, the first input by the information input device, the power conversion RGB to grayscale, the storage unit of the first frame, the storage unit of the second frame, the storage unit of the third frame, the output of block storage sequence is connected to the input of the power conversion RGB to grayscale, the output of which is connected to the input of the storage unit of the first frame, wherein the first output of the storage unit of the first frame connected to the first input of the first unit calculating the difference; the second output of the storage unit of the first frame is connected to the input of the first delay unit, the output of which is connected to the input of the storage unit of the second frame, the first output of which is connected to the second input of the first unit calculating the difference; the second output of the storage unit of the second frame connected to the first input of the second unit calculating the difference; the third output of the storage unit of the second frame connected to the input of the second delay unit, the output of which is connected to the input of the storage unit of the third frame, the output of which is connected to the second input of the second unit calculating the difference, the output of which is connected to the input of the second block binarization, the output of which is connected to the input of the second block delete objects in a small area, in the course of which is connected to the input of the second storage unit, the first output of which is connected to the input of the second computing unit variance, the second output of the second storage unit connected to the first input of the second unit of comparison, the output of the second computing unit variance connected to the second input of the second unit of comparison; the output of the first unit calculating the difference connected to the input of the first block binarization, the output of which is connected to the input of the first block delete objects in a small area, the output of which is connected to the input of the first storage unit, the first output of which is connected to the input of the first computing unit variance, the second output of the first storage unit connected to the first input of the first unit of comparison, the output of the first computing unit variance connected to the second input of the first unit of comparison; to the third inputs of the first and second units of comparison is connected to the output of the threshold value; the output of the first unit of comparison is connected to the input of the first block region-growing, the output of which is connected to the first input of the exclusive or; a second output unit connected to the comparison input of the second block region-growing, the output of which is connected to the second input of the "exclusive or", the output of which is connected to the input of the storage unit mask with the detected defects, the output of which is an information output device; synchronicity works the disorder is provided by clock pulses.



 

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8 cl, 7 dwg

FIELD: information technology.

SUBSTANCE: method involves parallel processing of the component of each decomposition level; brightness-contract transformation parameters are determined by forming a function for correcting brightness levels and a function for correcting contrast, forming a matrix of correction factors for third level decomposition contrast using the function for correcting contrast, reconstructing the family of matrices of scaled contrast correction factors for spatial matching on each level of correction factors with values of the detail component.

EFFECT: high quality of displaying digital images.

8 dwg

FIELD: information technologies.

SUBSTANCE: method includes performance of the following operations: digital copy of initial printed document is produced in colour space of RGB, brightness difference is detected, and direction of maximum gradient is determined, current count of image is classified for its affiliation to area of brightness difference or uniform area without sharp changes of brightness, Gauss smoothening of current count is made, if it is classified as belonging to uniform area without sharp changes of brightness, current count is smoothened in anisotropic manner, if it is classified as belonging to the area of brightness difference.

EFFECT: invention makes it possible to carry out fast single-stage descreening of screen-type pattern images with preservation of contour differences and increased accuracy.

5 cl, 9 dwg

FIELD: information technologies.

SUBSTANCE: target image that forms video image is divided into multiple division areas (DA); pass band (PB) width applied to DA is determined; array of filtration ratios (FR) is calculated to realise frequency characteristics corresponding to limitation of band, with application of PB width; image data is filtered with application of FR array; error information value is produced between obtained data and data of initial image, and distribution ratio (DR) is calculated to be used to determine optimal width of PB, on the basis of produced value; optimal width of PB corresponding to DR is defined for each DA, and array of optimal FR is calculated to realise frequency characteristics corresponding to limitation of band, using optimal width of PB; image data of division area is filtered using array of optimal FR; and produced data of each DA are synthesised.

EFFECT: generation of filtered image with specified value of image quality assessment.

29 cl, 27 dwg

FIELD: information technology.

SUBSTANCE: first band pass (BP) is determined based on initial image data; a matrix of filter coefficients (FC) is calculated to obtain frequency characteristics corresponding to limitation of frequency band (FB) using the first BP; data of the first filtered image are generated by filtering data of the initial image using the matrix of first FC; an estimate value of the objective image quality of data of the first filtered image is obtained and the distribution coefficient (DC) is calculated, which is used to determine the optimum BP based on the estimate value of objective image quality; the optimum BP corresponding to the calculated DC is determined using a table in which the corresponding relationship between DC and optimum BP is defined; a matrix of optimum FC is calculated to obtain frequency characteristics corresponding to limitation of FB using the optimum BP; and data of the optimally filtered image is generated by filtering data of the initial image using the matrix of optimum FC.

EFFECT: adaptive image filtering process for providing high-quality image.

3 cl, 11 dwg

FIELD: digital processing of images, possible use for global and local correction of brightness of digital photographs.

SUBSTANCE: system and method for correcting dark tones in digital photographs contain global contrasting module, module for conversion from RGB color system, module for determining dark tone amplification coefficient, bilateral filtration module, dark tone correction module, module for conversion to RGB color system, random-access memory block, displaying device. Global contrasting module is made with possible correction of global image contrast, module for conversion from RGB color system is made with possible conversion of image from RGB color system to three-component color system, one component of which is image brightness, and two others encode color, module for conversion to RGB color system is made with possible conversion from three-component color system, one of components of which is image brightness, and two others encode color, back to RGB color system, module for determining dark tone amplification coefficient is made with possible computation of global image brightness bar graph and can determine dark tone amplification coefficient based on analysis of signs, calculated from global image brightness bar graph, bilateral filtration module is made with possible execution of bilateral filtration of image brightness channel, dark tone correction module is made with possible correction of dark tones in image brightness channel.

EFFECT: absence of halo-effect.

2 cl, 17 dwg

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