Device for detecting defects on archival photographs

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to computer engineering and can be used in image analysis and processing systems and in digital television. The device comprises a preprocessing unit 1, a control unit 2, a first convolution computing unit 3, a second convolution computing unit 4, a second Gabor mask generating unit 5, a first Gabor mask generating unit 6, a first binarisation unit 7, a second binarisation unit 8, a first mask storage unit 9, a second mask storage unit 10, an adder 11, a post-processing unit 12, a resultant defect mask storage unit 13.

EFFECT: detecting the position of defects on archival photographs with insufficient prior information on statistical characteristics of distortions.

2 dwg

 

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

The image is considered as a simplified mathematical model, which represents a two-dimensional discrete sequence of Yi,j,i=1,N,j=1,Mthe form:

Yi,j=(1-di,j)Si,j+di,jci,j,

where Yi,jthe observed image, Si,j- the original (uncorrupted) image, di,jthe binary mask region with distorted values (1 corresponds to the distorted pixels, 0 is not distorted pixels), ci,j- distorted pixel values.

The main task is the detection of the position of the defects on archival fot the monographs.

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

Currently, there are a large number of methods for automatic detection of defects on the image. The first and most simple to implement algorithms belong to the class of threshold processing [Koutsopoulos H. and A. Downey Primitive-based classification of pavement cracking images. Journal of Transportation Engineering, 119(3), pp.402-418, 1993]. The main disadvantage of these methods is the dependence of the number of false positives from the threshold value. Another class of detectors is morphological image processing [Tanaka N. and K. Uematsu A crack detection method in road surface images using morphology. In Machine Vision Applications, pp.154-157, 1998]. For detection of defects on the photographs provided pre-filtering is used morphological operations to detect light and dark cracks respectively [Giakoumis I., Nikos Nikolaidis N. and Pitas I. Digital image processing techniques for the detection and removal of cracks in digitized paintings, 2005]. Also you can use different combinations of morphological operations. This class of detectors is in the resulting mask defects fewer false positives than detectors based on threshold processing, but no man is e, the efficiency also depends on a priori specified parameters.

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 image).

There is a method of ranking pixels (ROD) 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 on Yi,j. These pixels are taken from considerations of the motion compensation on the previous and subsequent frames, in places spatially close to the pixel at Yi,jand its two nearest vertical neighbors. Let dmthe sequence of pixels prsorted by rankd1d2...d6. Heredmeanthe average value of the sequence and rank-ordinal differencesROD(ηi ,j,l)where l=1,2,3...:

dmean=d3+d42,

ROD(ηi,j,l)={dl-Yi,j(ηi,j)if Yi,j(ηi,j)dmeanYi,j(ηi,j)-d7-lif Yi,j(ηi,j)>dmeanwith l=1,2,3.

The defect is detected if at least one of the ranked sequence exceeds the threshold T l. The threshold Tlis specified by the user, and determining the sensitivity of the detector [Nadenau M. J. and S. K. Mitra, "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)={1if ROD(ηi,j,l)>T0elsewith 0T1T2T3and l=1,2,3.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: threshold image processing, obtaining a 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 rangaramanuja ROD detector (SROD) [van Roosmalen, P. M. B., Biemond J., 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 possible 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(ηi,j)if min(pr)-Yi,j(ηi,j)>0Yi,j(ηi,j)-max(pr)if Yi,j(ηi,j)-max(pr )>0 0otherwisewithr=1,...,6..

The defect is detected, provided that:

LSROD(ηi,j)={1if SROD(ηi,j)>T10otherwisewith T10

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 far enough outside of rank.

The characteristics of the method-analogue, coinciding with the characteristics of the proposed technical solution, the following: threshold image processing, obtaining a 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 will be a lot of the positives you. 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.

The known method the threshold image processing [N. Otsu (1979), "A Threshold Selection Method from Gray-Level Histogram", IEEE Transaction on Systems, Man, and Cybernatics, vol. SMC-9, no.1, pp.62-66, January 1979].

The method is to select the optimal threshold, which would divide the intensity value of the image in shades of gray into two classes: the pixels belonging to the background, and pixels belonging to foreground objects. For correct operation of this algorithm assumes that the image histogram separably.

For optimal separation histogram iteratively selects a threshold value that minimizes the variance within a class, which is defined as the weighted sum of the variances of the two classes:

σw2(t)=w1(t)σ12(t)+w 1(t)σ22(t),

where w1is the probability of the two classes separated by a threshold t,σi2the dispersion of these classes. The Father showed that minimizing the variance within a class is equivalent to maximizing the variance between classes, which is equal to:

σb2(t)=w1(t)w2(t)(μ1(t)-μ2(t))2,

where µ1, µ2- the expectation of two classes.

Signs of a device similar to the matching characteristics of the proposed technical solution, the following; thresholding the image, creating a mask with defects.

The disadvantages of the method are as follows: false alarm detector on images containing noise component, also this al is orithm in most cases requires further post-processing the resulting mask to reduce false positives.

A device for detecting scratches on the image [US 20130034298 A1]. Search for defects in the image can be divided into three stages. The first step is the segmentation of the image. This operation is required for the localization of the defect on the image and follow up with him.

The second stage involves morphological processing with structural elements. Structural elements are represented as lines with different angles. This operation allows to get the best response on a linear objects and less on point. This operation reduces the number of false positives. After morphological processing binarization image specified threshold value. Also, the threshold value can be obtained by the method of the Father.

At the third stage for the mask with the alleged defects are calculated descriptors. In the present work, we have used morphological features of the alleged defects: defect area, the convexity coefficient, correlation coefficient, compactness, eccentricity of the ellipse. Then these descriptors come on nonlinear SVM classifier, which separates the pixels belonging to the defect or object.

Signs of a device similar to the matching characteristics of the proposed technical solution, the following:

- convolution with a variety of the cores, getting a mask with defects.

The disadvantages of the device similar to as follows:

- large computational costs, are not always accurate detection of defects.

- a necessary stage in the learning algorithm, limiting the scope of the detector.

Structural diagram of a device similar to contains the preprocessing unit, the registration unit image, the preprocessing unit, the block segmentation, extraction block descriptor block classification.

Closest to the invention is the imaging device on the basis of the modified Gabor filter [US 20060269160 A1]. Your prototype is implemented by convolution of the image Yi,jwith a set of Gabor kernels that allow for the textural analysis of the original image. The Gabor filter is a linear filter, the impulse transient response is defined as a harmonic function multiplied by a Gaussian. This filter is widely used for the detection of object boundaries and identify areas of the texture.

The Gabor function has the following form:

g(x,y;λ,θ,ψ,σ,γ)=exp(-x'2+γ2y '22σ2)cos(2πx'λ+ψ),

wherex'=xcosθ+ysinθ,y'=-xsinθ+ycosθ,

λ is the wavelength of the multiplier-cosine

θ is the orientation of the normal to the parallel stripes of a Gabor function in degrees

ψ is the phase shift in degrees,

γ is the compression ratio, which characterizes the ellipticity of the function.

Convolution is implemented by the following expression:

S=Y*g.

After applying Gabor filter each pixel of the image gives the feature vector. This vector signs effectively describes the local texture of the surrounding area of the pixel. The Gabor filters have geometric stability, namely, they are resistant to scaling, rotation, change the brightness or contrast.

The features of device-prototype matching characteristic and the proposed technical solution, the following: convolution of the image with the Gabor kernels.

The disadvantages of the known devices of the prototype are:

- inability to detect defects in the images, due to the fact that the device is used as the implementation of schemes texture analysis for image pixels.

The structural scheme of the device of the prototype contains a preprocessing unit, a control unit, the unit generating the first mask of Gabor, the block of the second generation of the Gabor mask, the first computing unit convolution and a second convolution computing unit, the block of post-processing.

The proposed device defect detection in archival photos allows you to automatically detect defects on archival photographs in a limited amount of a priori information.

The device implements the method of texture analysis applied to the problem at hand, which is based on the analysis result of the convolution of the original image Yi,jwith Gabor kernels. The first stage is applied to the image preprocessing, which consists in subtracting from the image average brightness level.

The Gabor function has the following form:

g(x,y;λ,θ,ψ,σ,γ)=exp(-x' 2+γ2y'22σ2)cos(2πx'λ+ψ),

wherex'=xcosθ+ysinθ,y'=-xsinθ+ycosθ,

λ is the wavelength of the multiplier-cosine

θ is the orientation of the normal to the parallel stripes of a Gabor function in degrees

ψ is the phase shift in degrees,

γ is the compression ratio, which characterizes the ellipticity of the function.

Based on the conducted research the following options are selected Gabor functions: two orientations of normals θ equal to 90° and 180°, the window size 5×5 pixels. Examples of Gabor functions with angle θ=90° and θ=180° is shown in Fig.1.

The choice of window size 5×5 is due to the fact that the cracks in the image have a structure of fine lines, while increasing the size of this parameter distortion in the form of additional oscillations around the alleged defects, which reduces the probability of correct detection. The parameter θ, R is wny 90° and 180°, provides the greatest versatility, which in most cases is sufficient to detect defects on the image.

After finding convolution with each mask applies to the operation of the binarization necessary in order to highlight areas that received the most response. The result is two masksM1i,jandM2i,j. Because you are using a kernel size of 5×5 pixels, the filter has only one extremum, resulting in a large response gain and steady-state light areas. The obtained mask pixel are summed, that is, applies a logical OR operation, resulting in maskMi,j.

Using the resulting binarytreeMi,jresponse as mask defects is not possible. To reduce the number of false positives to an array ofMi ,japplies the operation to erosion. As a result of this morphological operations resulting array is formed defectsMi,jwhere as 1 marked the position of the defects on the source image.

Device defect detection on archival photographs contains (Fig.2) the preprocessing unit 1, the first input by the information input device, the first output of which is connected to the first input of the first convolution computing unit 3, the output of which is connected to the input of the first block binarization 7, the output of which is connected to the input of the first storage unit mask 9, the output of which is connected to the first input of the adder 11; the second input of the preprocessing unit 1 is connected to the first output control unit 2, the second output of which is connected to the output unit of the second generation of the Gabor mask 5, the output of which is connected to the second input of the second convolution computing unit 4; the third output of the control unit 2 is connected to the input of the first generation of the Gabor mask 6, the output of which is connected to the second input of the first convolution computing unit 3; the second output of the preprocessing unit 1 is connected to the first input of the second block vechicle the Oia convolution 4, the output of which is connected to the input of the second block binarization 8, the output of which is connected to the input of the second storage unit of the mask 10, the output of which is connected to the second input of the adder 11, the output of which is connected to the input of the postprocessing unit 12, the output of which is connected to the input of the storage unit of the resulting mask defects 13, the output of which is an information output device.

Device defect detection on archival photographs of works in the following way. The original image is fed to the input of the preprocessing unit 1, which offset the impact of lighting on the result of processing by subtracting from the original image average brightness level. The control unit 2 sets the parameters for the preprocessing unit 1, the generation parameters of the first and second masks Gabor. In the blocks of the first generation of the Gabor mask 6 and the second of the Gabor mask 5 formed the coefficients of the Gabor kernels, computed for the two parameters θ equal to 90° and 180°, respectively. The system calculates convolution preprocessed image with two cores Gabor in the first computing unit 3 convolution and a second convolution computing unit 4, respectively. Next, the result of the convolution for each core binarized by the threshold processing in the first and second blocks of binarization 7 and 8. The result of binarization is written in the first and second blocks storing the value of the mask 9 and 10. For processed mask is the logical sum is formed maskMi,jin the adder 11. To reduce the number of false positives to an array ofMi,japplies the operation to erosion in the postprocessing unit 12. As a result of this morphological operations resulting array is formed defectsMi,jwhere as 1 marked the position of the defects on the original image. The processing result is recorded in the storage unit of the resulting mask defects 13.

The technical result is the detection of the position of the defects on archival photographs in the conditions of insufficient a priori information about the statistical characteristics of the distortion.

Device defect detection on archival photographs with the preprocessing unit, the first input by the information input device, the first output of which is connected to the first input of the first convolution computing unit; a second input preprocessing unit connected to the first output control unit, the second output to the th is connected to the output unit generating second mask of Gabor, the output of which is connected to the second input of the second convolution computing unit; the third output control unit connected to the input unit of the first generation of the Gabor mask, the output of which is connected to the second input of the first convolution computing unit; the second output of the preprocessing unit connected to the first input of the second convolution computing unit; a block of post-processing, wherein the output of the first convolution computing unit connected to the input of the first block binarization, the output of which is connected to the input of the first storage unit mask, the output of which is connected to the first input of the adder; the output of the second convolution computing unit is connected to the input of the second block binarization, the output of which is connected to the input of the second storage unit mask, the output of which is connected to the second input of the adder, the output of which is connected to the input of the block of post-processing, the output of which is connected to the input of the storage unit of the resulting mask defects, the output of which is an information output device.



 

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SUBSTANCE: invention concerns technology of video compression, particularly deblocking filters. Invention claims deblocking filter applied in videocoder/videodecoder based on multiple layers. Process of deblocking filter power (filtration power) selection during deblocking filtration in respect of margin between current block encoded in intra-BL mode and adjoining block involves determination of whether current or adjoining block has coefficients. Filter power is selected as first filter power if current or adjoining block features coefficients; and filter power is selected as second filter power if current or adjoining block does not have coefficients, So that first filter power exceeds second filter power.

EFFECT: enhanced efficiency of video deblocking.

22 cl, 13 dwg

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