A method for improving the quality of a two-dimensional image of a three-dimensional object, and the corresponding data processing system and device.

Abstract

Method for improving the quality of a two-dimensional image of a three-dimensional object, and corresponding data processing system and device. The present invention relates to a method (100) for improving the quality of a two-dimensional image (IM_in) of a three-dimensional object (15) for the purpose of positioning a defect (12) comprising supplying as input an initial two-dimensional image (IM_in) of the object (15), of a minimum distance (Dmin(Δi)) and of a maximum distance (Dmax(Δi)) between the object (15) and a source (25) along at least one projection direction (Δi);for a plurality of reference distances (Dref,i,j) relative to the source (25) along the projection direction (Δi), the determination (110) of at least one respective geometric point spreading function (PSFi,j) associated with a representative model of the radiation source (25) for a reference point virtually positioned in the object (15) at said reference distance (Dref,i,j) along the projection direction (Δi); the generation (120) of respective two-dimensional deconvolved images (IM_deconv,i,j) of the initial image based on the respective geometric point spreading function (PSFi,j); and, as output, the provision of a two-dimensional final image (IM_fin) selected from the generated images and / or the respective reference distance (Dref,fin) associated with the final image. Figure for the abstract: 4;

Description

Title of the invention: Method for improving the quality of a two-dimensional image of a three-dimensional object, and corresponding data processing system and device

[0001] The present invention relates to a method for improving the quality of a two-dimensional image of a three-dimensional object and the corresponding data processing system and device.

[0002] Industrial radiography is a non-destructive testing method used in many fields to search for the presence of possible defects in industrial parts and structures, both during manufacturing and during maintenance operations.

[0003] For example, the presence of cracks in a pipe or in a steam generator tank can be detected by radiography.

[0004] Industrial radiography includes the irradiation of an object to be inspected by means of a source of penetrating radiation and the obtaining of an image by detection of the radiation transmitted by the object.

[0005] The radiations are for example X-rays or gamma rays.

[0006] Tomographic methods include obtaining a plurality of images of the object by moving the source-receiver pair relative to the object.

[0007] Document EP 3 157 017 describes a high-resolution radiography method. This method includes acquiring at least two initial images of an object of interest under different conditions, for example with radiation of different energies, and generating a synthetic image from slices of each of the two initial images, so as to accurately determine the three-dimensional position of a defect in the object of interest.

[0008] However, the process described by EP 3 157 017 requires the acquisition of a plurality of images of the object, therefore several irradiations of this object.

[0009] Furthermore, if the source and / or the detector are far from the object, the intensity of the irradiations must be relatively high so that the images obtained are sufficiently contrasted.

[0010] On the contrary, if the source is close to the object and / or the object is far from the detector, the intensity of the irradiations may be lower, but the geometric blur induced by the positioning of the object relative to the source and / or the detector impairs the accuracy of the image obtained and thus reduces the accuracy of the positioning of any defects in the object.

[0011] An object of the invention is then to propose a method for improving the quality of an initial two-dimensional image obtained by irradiating an object of interest with a radiation source, the method making it possible to detect a defect or inhomogeneity in the object of interest, and where appropriate, to position this defect or inhomogeneity in depth along a projection direction from a single image, including when the relative positions of the radiation source and / or the object and / or the detector result in the presence of significant geometric blurring in the initial image.

[0012] To this end, the invention relates to a method for improving the quality of a two-dimensional image of a three-dimensional object for the purpose of detecting and positioning a defect or inhomogeneity in the object, the method comprising: i) the provision as input of an initial two-dimensional image of the object, the initial image being obtained by: - irradiation of the object by means of a radiation source emitting radiation towards the object in at least one direction of propagation, and - detection by means of a radiation detector comprising a two-dimensional detection surface of the radiation transmitted by the object; ii) the provision at the input of a minimum distance and a maximum distance between the object and the source in at least one projection direction; iii) for a plurality of reference distances from the source along the projection direction, the reference distances being between the minimum and maximum distances, the determination of at least one geometric spread function of the respective point associated with a representative model of the radiation source for at least one reference point positioned virtually in the object at said reference distance along the projection direction; (iv) for each reference distance, the generation of a respective two-dimensional deconvolved image of the initial image by deconvolving the initial image based on the respective geometric spreading function of the point; and (v) as output, the provision of: * of a final two-dimensional image selected from the set of said deconvoluted images generated by means of at least one predetermined selection criterion which is a quantitative parameter for evaluating the sharpness of the deconvoluted images, and / or * of the respective reference distance associated with the final image selected as a result of a measurement of the position of the inhomogeneity or defect sought in the object.

[0013] The common practice in the field of industrial radiography is to position the source and the detector relative to the object of interest in such a way as to minimize geometric blur.

[0014] Unexpectedly, the inventors observed that the physical process of forming, on the surface of the detector, the image of a defect or inhomogeneity of an object can be mathematically modeled by a convolution operation based on the spreading function of the point, including when the radiation source is extended and of significant dimensions considering the resolution of the detector in a given detection position.

[0015] Consequently, if the position of the defect / inhomogeneity is precisely known, the deconvolution of the initial image makes it possible to obtain a clear image independently of the relative positions of the source, the defect / inhomogeneity and the detector.

[0016] The deconvolution matrix to be used for the deconvolution operation depends, however, on the position of the defect / inhomogeneity within the object, which is by definition unknown since the existence of the defect / inhomogeneity and its position are precisely what we seek to determine by the process.

[0017] The method according to the invention makes it possible to circumvent this problem by generating a plurality of deconvolved simulated images from the initial image, the deconvolution being carried out on the basis of the spread function of the point for a plurality of reference positions assumed for the defect or inhomogeneity sought, followed by the selection of only one of these simulated images on the basis of a predetermined selection criterion.

[0018] Indeed, the inventors have shown that the simulated image considered to be the sharpest with respect to the predetermined selection criterion is that which is obtained for the reference position closest to the actual position of the defect or inhomogeneity according to the direction of projection.

[0019] The method according to the invention therefore makes it possible to observe and position with precision a defect or inhomogeneity in a three-dimensional object from a single initial image.

[0020] The method according to the invention can be implemented without resorting to any geometric deblurring of the initial image. It is therefore possible to position the radiation source closer to the object than in prior art methods. The method according to the invention thus makes it possible to reduce the intensity of the radiation used for image acquisition while maintaining very good positioning accuracy for the defects.

[0021] The method according to the invention also allows for better accuracy in the search for defects or homogeneity for a source position identical to that of the prior art processes, especially when the source is used in a panoramic configuration such as within a pipeline.

[0022] According to other advantageous aspects of the invention, the method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0023] - the source is spatially extended and comprises a plurality of source points which can be separated taking into account a resolution and a position of the detector relative to the source;

[0024] - no step of reducing or eliminating geometric blur is put into artwork ;

[0025] - each deconvolved image is generated using a deconvolution matrix respective which is determined from the geometric spreading function of the respective point for each reference distance along the projection direction;

[0026] - the method includes determining the deconvolution matrix for each reference distance along the projection direction, this determination including: a) modeling the source by means of a cloud of a plurality of discrete source points; b) the determination of a grid tiling at least part of the detection surface and comprising a plurality of pixels; c) for each reference distance along the projection direction: cl) the positioning of a reference point in the object at the reference distance from the source along the projection direction, c2) for each source point, the determination of a respective simulated image point which is, if it exists, a point of intersection with the detection surface of a respective straight line passing through the source point and the respective reference point, c3) for each pixel of the grid, the determination of a respective image density, comprising the calculation of a ratio between a total number of simulated image points contained in said pixel and a total number of simulated image points contained in the grid, each of the coefficients of the deconvolution matrix being determined from a respective image density;

[0027] - the determination of each coefficient of each deconvolution matrix includes the weighting of the respective image density based on a physical radiation attenuation effect associated with a thickness of the object traversed along the respective line and / or with a nature of the radiation emitted by the source, and / or based on a geometric attenuation effect associated with a position of each respective source point relative to the respective pixel;

[0028] - the predetermined selection criterion is chosen from a maximum of a gradient of contrast and / or a contrast-to-noise ratio of the deconvolved image;

[0029] - the method comprises providing as output a plurality of final images and / or of respective reference distances, each obtained for a respective projection direction;

[0030] - the method includes analyzing the initial image using a criterion of predetermined homogeneity, and only if a defect or inhomogeneity is detected at the end of the analysis, the implementation of steps ii) to v) on at least a portion of interest of the initial image including the detected defect or inhomogeneity;

[0031] The invention also relates to a data processing system comprising means for implementing the steps of the process according to any one of the preceding embodiments.

[0032] According to another advantageous aspect of the invention, the data processing system comprises the following feature:

[0033] - an image analysis module configured to implement steps iii), iv) and (v) the process according to any one of the preceding embodiments.

[0034] The invention also relates to a device for detecting inhomogeneity or defects in an object of interest, the device comprising:

[0035] - a data processing system according to any one of the modes of previous achievements - a source of radiation, and

[0036] - a radiation detector comprising a detection surface two-dimensional radiation.

[0037] According to another advantageous aspect of the invention, the detection device comprises the following feature:

[0038] - the source is a source of penetrating radiation chosen from a source Iridium 192 (192Ir), a Selenium 75 (75Se) source, a Cobalt 60 (60Co) source and an X-ray source.

[0039] The invention also relates to a data processing system comprising means for implementing the steps of the process according to any one of the preceding embodiments.

[0040] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the figures in which:

[0041] [Fig-1] [Fig.1] represents a radiation source placed on the axis of an object cylindrical object of interest comprising a hypothetical point defect M' and a detector placed near an external surface of the object of interest, in a cutting plane orthogonal to the axis of the cylinder;

[0042] [Fig.2] [Fig.2] represents the elements of [Fig.1] in a cutting plane including the axis of the cylindrical object and at least part of the detector;

[0043] [Fig.3] [Fig.3] is a schematic example of the final image obtained by the process according to the invention in the situation shown in Figures 1 and 2;

[0044] [Fig.4] [Fig.4] represents an embodiment of the process according to the invention in the form of a flowchart;

[0045] [Fig. 5] [Fig. 5] illustrates the principle of obtaining the geometric point spread function (PSF) for a cylindrical radiation source. The image densities associated with each pixel for a grid of pixels tiling the detector's detection surface are obtained by projecting each element / point composing the source through a hypothetical point defect located inside the inspected object;

[0046] [Fig.6] [Fig.6] is a three-dimensional view of the elements shown in [Fig.2], in the case of a known spherical defect, located at a known distance of 450 mm from the source along the reference direction Al;

[0047] [Fig. 7] [Fig. 7] shows a) an ideal image that would be obtained in the case of [Fig. 6] if the source 25 were a point source positioned at the center of the cylindrical source shown in [Fig. 6], b) a real image obtained in the configuration of [Fig. 6], and c) an image of the geometric spreading function of the point obtained in the configuration of [Fig. 6] and according to the principle described in [Fig. 5]; and

[0048] [Fig.8] [Fig.8] groups the images obtained by deconvolution of image b) of [Fig.6] for five simulated positions of the spherical defect 12 different with respect to the source, namely a distance Dref,i,j(Ai) respectively equal to a) 410 mm, b) 430 mm, c) 450 mm corresponding to the real position of the defect 12 of [Fig.6], d) 470 mm and e) 490 mm.

[0049] The invention relates to a device for detecting an inhomogeneity or defect 12 in an object 15 of interest, the detection device 10 comprising: - a data processing system 20, - a radiation source 25, and - a radiation detector 30 comprising a two-dimensional detection surface 30A of the radiation emitted by the source 25.

[0050] Object 15 is for example a pipe or a tank, in particular a steam generator tank.

[0051] As can be seen in the partial cross-sectional view of [Fig.1], the object 15 can be cylindrical, the axis of the cylinder extending along a longitudinal direction Z.

[0052] Object 15 can be delimited by a wall 35 of thickness e.

[0053] The wall thickness e is constant or variable.

[0054] In the case of [Fig.1], the wall 35 has a constant thickness e.

[0055] On [Fig.1], the positions are located in a cylindrical coordinate system r, 0, Z with axis the longitudinal direction Z of the cylindrical object 15.

[0056] In the case of [Fig.1], the wall 35 extends between an internal surface 35A, defined by the equation r = r_min, and an external surface 35B, defined by the equation r=r_max.

[0057] Object 15 may be made, for example, from a material chosen from a metallic material, a ceramic material, a plastic material, or any other material.

[0058] The object 15 may include one or more 35C welds, such as the 35C weld represented by hatching on [Fig.2].

[0059] In figures 1 and 2, a hypothetical defect or inhomogeneity 12 has been shown around point M' of weld 35C.

[0060] The defect 12 is, for example, a crack in the object 15, in particular in the weld 35C. The inhomogeneity 12 is, for example, an inhomogeneity in the chemical composition and / or, where applicable, in a crystalline structure of this material.

[0061] In the following, we will refer to defect 12 for the sake of simplicity, but all of the following may apply to the case of inhomogeneity 12 and vice versa, unless explicitly stated otherwise.

[0062] If the defect 12 exists, its position M', its shape and its dimensions are generally not known a priori, insofar as the defect 12 may not be observable to the naked eye and / or may be positioned inside the wall 35 at an unknown depth r(M').

[0063] The detection device 10 is therefore intended to enable the detection of a possible defect 12 without prior knowledge of this defect and the positioning of this defect 12 within the object 15.

[0064] The radiation source 25 is preferably chosen according to the nature of the object 15 to be analyzed.

[0065] The source 25 is for example a source of penetrating radiation, capable of passing through the material or materials of which the object 15 is composed.

[0066] By way of example, source 25 is a gamma radiation source. It may in particular be an Iridium 192 (192Ir) source, or a Selenium 75 (75Se) source, or a Cobalt 60 (60Co) source.

[0067] Source 25 can be an X-ray tube.

[0068] The source 25 can be a linear particle accelerator.

[0069] The detector 30 includes the two-dimensional detection surface 30A, configured to detect the radiation emitted by the radiation source 25.

[0070] The detector 30 is configured to generate a two-dimensional initial IM_in image of the object 15 when this object 15 is irradiated by the source 25 according to at least one direction of propagation of the radiation emitted by the source 25, and to transmit the initial image IM_in to the data processing system 20.

[0071] The transmission of the IM_in image can be done by a wired or wireless transmission system (not shown). In particular, the data processing system 20 can be located remotely from the detector 30. The data processing system 20 and the detector 30 can, in particular, be located in different places.

[0072] The data processing system 20 is a device configured to implement the method 100 to improve the quality of the initial two-dimensional IM_in image for the purpose of searching for and positioning the defect or inhomogeneity 12 in the object 15 according to the invention.

[0073] With reference to [Fig. 4], process 100 comprises: i) the input of the initial two-dimensional image IM_in; ii) the provision at input of a minimum distance Dmin(A;) and a maximum distance Dmax(Ai) between the object 15 and the source 25 along at least one projection direction A;; iii) for a plurality of reference distances Dref,i,j, along the projection direction A;, between the minimum distance Dmin(Ai) and maximum distance Dmax(Ai), the determination 110 of at least one geometric spreading function of the respective point PSFi,j associated with a representative model of the radiation source 25 for at least one reference point virtually positioned in the object 15 at said reference distance Dref,i,j along the projection direction A;; iv) for each reference distance Dref,i,j, the generation 120 of a respective two-dimensional deconvolved image IM_deconv,i,j of the initial image IM_in by deconvolution of the initial image on the basis of the respective geometric spreading function of the point PSFi,j; and v) as output: - the provision of a final two-dimensional image IM_fin selected from the set of said deconvoluted images IM_deconv,i,j generated by means of at least one predetermined selection criterion which is a quantitative parameter for evaluating the sharpness of the deconvoluted images IM_deconv,i,j and / or - the provision of the reference distance Dref,fin associated with the final selected image as a result of a measurement of the position of the defect 12 sought in the object 15.

[0074] The process 100 is implemented by the data processing system 20.

[0075] In particular, the data processing system 20 may include an image analysis module 20A configured to implement steps iii), iv) and v).

[0076] The initial image IM_in can be obtained at the end of an acquisition step 140 of the initial image IM_in, by irradiating at least a portion of interest ROI of the object 15 by means of the source 25 and detecting by means of the detector 30 the radiation emitted by the source 25 after interaction with the object 15.

[0077] The acquisition step 140 is carried out upstream of the initial image supply step IM_in, at a site remote or not from the site of implementation of the other steps of the process 100.

[0078] The implementation of the supply step can be implemented immediately after the acquisition step 140 or at a later time, the time separating these two steps having no impact on the result of the process 100.

[0079] The interaction with the object 15 is for example a transmission of the radiation emitted by the source 25 by the object 15.

[0080] To do this, as shown in [Fig.2], the source 25 is positioned at an appropriate distance from the object 15 and in such a way that at least part of the radiation from the source 25 propagates to the portion of interest ROI.

[0081] The source 25 is, for example, positioned on the side of the internal surface 35A

[0082] The method 100 can be implemented for a range of positions of the source 25 compared to object 15, which is more extensive than the processes of the prior art.

[0083] As in prior art processes, the position of the source 25 can be chosen so as to minimize geometric blur, but this is not necessary for the operation of the process 100.

[0084] Alternatively, the source 25 can be positioned close to the object 15.

[0085] In a particular embodiment, the source 25 can be positioned in a panoramic configuration. For example, as shown in [Fig. 1], if the object 15 is a cylindrical pipe, the source 25 can be placed on the longitudinal axis Z of the pipe.

[0086] The detector 30 is positioned so that the detection surface 30A detects at least a part of the radiation emitted by the source 25 after interaction with the portion of interest ROI, as seen in [Fig.2].

[0087] In particular, if the radiation is penetrating, the object 15 can be placed between the source 25 and the detection surface 30A.

[0088] Preferably, the detector 30 is positioned close to the external surface 35B.

[0089] The positions of detector 30 and source 25 can be interchanged.

[0090] The initial image IM_in is then transmitted by detector 30 to the system of data processing 20, remote or not.

[0091] The data processing system 20 receives the initial image IM_in and at least one projection direction Aj.

[0092] A given projection direction A; is a direction which passes through both a reference point Si of the source 25, for example the center Sc of the source 25 if it exists, as shown in [Fig.5], a respective point PAi of the internal surface 35A, a respective point PBji of the external surface 35B of the portion of interest ROI, and a point of the detection surface 30A, as seen in [Fig.5].

[0093] The data processing system 20 receives data relating to a minimum distance Dmin(Ai) and a maximum distance Dmax(A;) between the object 15 and the source 25 according to the projection direction A;.

[0094] The data processing system 20 typically receives a minimum distance Dmin(Ai) and a maximum distance Dmax(Ai) between the object 15 and the source 25 according to the projection direction A;.

[0095] The minimum distance Dmin(Ai) and the maximum distance Dmax(Ai) are known a priori. This is, for example, data provided by the manufacturer of the object 15 or obtained from a prior measurement step.

[0096] The minimum distance Dmin(A;) is the distance SiPA.i, i.e. r_min in the case of [Fig.1]. The maximum distance Dmax(A;) is the distance SiPB.i, i.e. r_max in the case of [Fig.1].

[0097] The representative model of the radiation source 25 is also provided to the data processing system 20.

[0098] The representative model of the radiation source 25 can be point-like, one-dimensional, two-dimensional or three-dimensional.

[0099] The representative model of the radiation source 25 is typically a cylinder.

[0100] The data processing system 20 receives or determines a plurality of reference distances Dref,i,j with respect to the source along the projection direction A; , j being an integer between 1 and a positive integer Ni.

[0101] The reference distances Dref,i,j are between the minimum distance Dmin(Ai) and the maximum distance Dmax(A;).

[0102] The reference distances Dref,i,j are for example equidistributed between the minimum distance Dmin(Ai) and maximum distance Dmax(Ai).

[0103] The integer Ni can be provided as input to the data processing system 20.

[0104] In a particular embodiment, the integer Ni can be chosen according to the difference between the minimum distance Dmin(A;) and the maximum distance Dmax(A;).

[0105] In a particular embodiment, the integer Ni is the same for all projection directions A; if applicable.

[0106] For each reference distance Dref,i,j along the projection direction Ai, the representative model of the radiation source 25 is used to calculate at least one geometric spreading function of the point PSFi,j for at least one point of reference virtually positioned at the reference distance Dref,i,j. The geometric spreading function of the point PSFi,j is configured to generate the projection of the representative model of the radiation source 25 by at least one virtual point positioned in the object 15 at the reference distance Dref,i,j along the projection direction A;.

[0107] In a particular embodiment, a single geometric spread function of the point PSFi,j is calculated for a single reference point positioned at the reference distance Dref,i,j.

[0108] This embodiment is particularly advantageous in the case where the geometric blur is essentially due to the size of the source 25 or in the case where the geometric spreading function of the point PSFi,j can be considered as locally invariant over the extension of the defect sought.

[0109] In a particular embodiment, a set of reference points whose relative positions are predetermined is virtually positioned at the reference distance Dref,i,j; that is, a particular point locating the position of the set of reference points is virtually positioned at the reference distance Dref,i,j. A geometric spreading function of the point PSFi,j is then calculated for each of the reference points. This embodiment is particularly advantageous when the geometric spreading function of the point PSFi,j varies significantly over the extent of the defect being sought.

[0110] The geometric spreading function of the respective point PSFi,j is then determined.

[0111] As described previously, the geometric spreading function of the point PSFi,j is configured to model the spatial distribution of the intensity of the radiation from the source 25 detected on the detection surface 30A after passing through a reference point virtually positioned in the object 15 at the reference distance Dref,i,j.

[0112] The geometric spreading function of the point PSFi,j therefore carries information on the shape and dimensions of the source 25 and on the reference distance Dref,i,j.

[0113] In a particular embodiment referred to as Example 1 hereafter, the source 25 is spatially extended, that is to say, it comprises a plurality of source points which can be separated taking into account a resolution and a position of the detector 30 relative to the source 25.

[0114] The determination of the geometric spreading function of the respective point PSFi,j can then include a step of modelling the source 25 by means of a cloud of a plurality of discrete source points Sk, k being an integer between 1 and a positive integer Q, as shown in [Fig.5].

[0115] For this purpose, the data processing system 20 can, for example, receive information on the dimensions, shape and position of the source 25 relative to the object 15, as well as the positive integer Q. On the basis of this information, the data processing system 20 can randomly determine the positions of Q source points Sk distributed in the source 25.

[0116] The source points Sk are advantageously distributed randomly in the source 25. This embodiment makes it possible to limit the time cost of the process.

[0117] The value of the integer Q can be chosen according to the dimensions of the source 25 and / or a time and / or memory cost of determining the geometric spreading function of the point PSFi,j for at least one respective reference distance Dref,i,j.

[0118] In the case of Example 1, the determination of the geometric spreading function of the respective point PSFi,j may also include the determination of a grid tiling at least a part of the detection surface 30A and comprising a plurality of pixels PIX(m,p), m and p being positive integers, as shown in [Fig.5].

[0119] For this purpose, the data processing system 20 can, for example, receive information on the dimensions, shape and position of the detection surface 30A relative to the object 15 and information on the dimensions of at least one given pixel PIX(m,p) and / or on a total number of pixels R. On the basis of this information, the data processing system 20 decomposes the detection surface 30A into a plurality of pixels PIX(m,p) equidistributed along each of two directions XI, Y1 of a plane in which the detection surface 30A extends.

[0120] In the case of [Fig.5], the detection surface 30A has been decomposed into a four-by-four grid formed of rectangular PIX(m,p) pixels.

[0121] In the case of example 1, once the plurality of pixels PIX(m,p) has been determined, for each reference distance Dref,i,j along the projection direction A;, a reference point Pi,j is virtually positioned in the object 15 at the respective reference distance Dref,i,j from the source 25 along the projection direction A;.

[0122] Then, in the case of Example 1, for each source point Sk, a respective simulated image point I_k,i,j is determined by the data processing system 20 from a known position of the detector 30. The simulated image point I_k,i,j is the point of intersection of a respective straight line passing through the respective source point Sk and the reference point Pi,j acting as a projection point with the detection surface 30A if this intersection exists. This situation can be visualized in [Fig. 5] for five examples of particular source points, namely S2, S3, Sk, Sk+1, and SQ.

[0123] It is understood that Example 1 makes it possible to determine a geometric spreading function of the point PSFi,j configured to model the spatial distribution of the intensity of the radiation from the source 25 detected on the detection surface 30A after interaction with the respective object 15_sim,i,j corresponding to object 15 if this object contained a point fault 12, or a fault 12 assimilable to a point at the resolution of the source-detector set, at the reference distance Dref,i,j.

[0124] Once the determination 110 of the respective point spreading geometric function(s) PSFi,j has been carried out, the process 100 includes the generation 120 of the respective two-dimensional deconvolved image IM_deconv,i,j of the initial image IM_in for each reference distance Dref,i,j along the projection direction Aj.

[0125] For this purpose, the data processing system 20 deconvolves the initial image IM_in on the basis of the geometric spreading function of the respective point PSFi,j.

[0126] The deconvolution operation takes into account the information on the reference distance Dref,i,j since it is based on the geometric spreading function of the respective point PSFi,j, which carries this same information.

[0127] The respective two-dimensional deconvolved image IM_deconv,i,j will therefore be of improved quality compared to the initial image IM_in, and in particular sharper than the initial image IM_in, if the real object 15 actually contains a defect 12 at the reference distance Dref,i,j.

[0128] On the contrary, if the real object 15 does not contain any defects, the deconvolution operation will not introduce any improvement in image quality.

[0129] For deconvolution, the data processing system 20 can determine a respective deconvolution matrix from the respective point spreading geometric function PSFi,j.

[0130] In the case of Example 1, the determination of the respective deconvolution matrix may include the determination by the data processing system 20 of a respective image density for each pixel PIX(m,p) of the grid.

[0131] The determination of the respective image density includes the calculation of a ratio between a total number of simulated image points I_k,i,j included in the respective pixel PIX(m,p) and a total number of simulated image points I_k,i,j included in the grid.

[0132] By way of example, if we consider for simplicity that the five simulated image points represented on [Fig.5] are the only simulated image points existing in this case, the image density of pixel PIX(3,3) is equal to 3 / 5, that of pixels PIX(3,4) and PIX(4,4) is equal to 1 / 5, and that of the other pixels is zero.

[0133] In the case of Example 1, the deconvolution matrix has the same dimension as the pixel grid, each of the coefficients of the deconvolution matrix being determined from the respective image density.

[0134] It is understood that calculating the respective image densities for each pixel makes it possible to go from an irregular distribution of simulated image points I_k,j,i to a regular distribution of pixels, suitable for use in deconvolution, all retaining some of the information contained in the respective point source spreading geometric function PSFi,j, in particular the shape and dimensions of the radiation source 25 and the respective reference distance Dref,i,j.

[0135] Optionally, the coefficients of the deconvolution matrix are determined from the respective image density after weighting on the basis of a physical radiation attenuation effect associated with a thickness of the traversed object along the respective line SkPi,j and / or with a nature of the radiation emitted by the source 25, and / or on the basis of a geometric attenuation effect associated with a position of each respective source point Sk with respect to the respective pixel PIX(m,p).

[0136] This arrangement allows for a better modeling of the physical interaction between the radiation from the source 25 and the object 15, thus enabling the acquisition of even higher-quality deconvolved images IM_deconv,i,j. In particular, depending on the direction of the respective line SkPi,j, the thickness of the object 15—for example, the wall 35—may vary, such that the intensity of the light ray following this direction to the detection surface 30A may be more or less attenuated or scattered. The contrast and sharpness of the IM_in image are affected by this attenuation or scattering, so that taking these physical effects into account during deconvolution allows, where appropriate, for a further improvement of the deconvolved image IM_deconv,i,j.

[0137] Once the deconvolved images IM_deconv,i,j have been generated, process 100 includes the selection of the final two-dimensional image IM_fin.

[0138] For this purpose, the data processing system 20 receives at least one predetermined selection criterion which is a quantitative parameter for evaluating the sharpness of deconvolved images IM_deconv,i,j.

[0139] The predetermined selection criterion can be chosen from a maximum of a contrast gradient and / or a contrast-to-noise ratio of the deconvolved image IM_deconv,i,j.

[0140] Then the data processing system 20 selects, from the set of said deconvolved images IM_deconv,i,j generated, the one that best satisfies at least one predetermined selection criterion, the selected image being the final image IM_fin.

[0141] For example, the final IM_fin image is that of the deconvolved images generated for the projection direction A; which has the highest contrast-to-noise ratio.

[0142] The respective reference distance Dref,i,j corresponding to the selected final image IM_fin is denoted Dref,fin.

[0143] The deconvolved images IM_deconv,i,j have an improved quality compared to the initial image if and only if a defect is actually present in the object 15 at the respective reference distance Dref,i,j along the projection direction A;, according to the improvement effect permitted by the deconvolution described above.

[0144] [Fig.8] allows us to understand the effect of the final image selection step IM_fin. This figure was obtained in a configuration shown in [Fig.6], in which the object 15 contains a known spherical defect 12, at a real distance Dref,reel (AJ) equal to 450 mm from the center Sc of the source 25 along the projection direction (Ai).

[0145] The initial image IM_in obtained in this configuration is visible on [Fig.7] b). It can be observed that this image is blurred compared with the simulated image visible on [Fig.7] a), which would be obtained if the source 25 used had been a point source positioned at the center Sc of the real source 25.

[0146] The deconvolved images IM_deconv,l,j were generated for five reference distances Dref,l,j (j varying from 1 to 5) equally distributed between the minimum distance Dmin(Ai) equal to 400 mm and the maximum distance Dmax(Ai) equal to 500 mm, namely respectively Dref,l,l(Al) = 410 mm, Dref,l,2(Al) = 430 mm, Dref,l,3(Al) = 450 mm = Dref,reel (Ai), Dref,l,4(Al) = 470 mm and Dref,l,5(Al) = 490 mm.

[0147] In this example, each of the deconvolved images IM_deconv,l,ja was generated using the respective source point geometric function PSFl,j calculated for the reference distance Dref,l,j(Al).

[0148] The [Fig.7] c) is a representation of the image of the geometric dispersion function of the source point PSF1,3 calculated for the reference distance Dref,l,3 (Al) = 450 mm in the plane of the detector.

[0149] It can be seen that figures 8 a), b), d) and e) are all less clear than [Fig.8] c) obtained when the reference distance is the real distance Dref,reel (Ai), at which the point defect is actually located.

[0150] It is therefore clear that the improvement in image quality is greater the closer the actual defect 12 is to the reference distance Dref,i,j chosen for the reference point.

[0151] In the case of [Fig.8], the final image IM_fin chosen would therefore be image c) of this figure, of improved quality compared to the image of [Fig.7] b).

[0152] The step of selecting the final image IM_fin from among the set of deconvolved images generated IM_deconv,i,j according to the projection direction A; based on the selection criterion, therefore makes it possible to find the most probable position of the defect 12 according to this projection direction A; without any prior knowledge of this position and without a geometric deblurring operation of the initial image IM_in being necessary.

[0153] The process 100 is therefore based on a complete paradigm shift compared to prior art processes, for which a preliminary geometric deblurring operation is necessary at least in cases where the source 25 is extended and / or too close to object 15. In process 100, the geometric blur is taken into account from the outset as such.

[0154] The method 100 is based on sliding a deconvolution kernel constructed on the basis of a point defect whose position is varied by simulation along a projection direction A;. This deconvolution operation makes it possible to virtually reconstruct information on the depth of a defect 12 sought.

[0155] The selection of the clearest final image IM_fin from among all the deconvolved images IM_deconv,i,j, images which each correspond to a particular hypothesis on a position of the defect 12 sought, makes it possible to conclude on the presence or not of such a defect and on the most probable position of this defect.

[0156] The data processing system 20 then provides as output the final image IM_fin, of improved quality compared to the initial image IM_in, and / or the respective reference distance Dref,fin associated with the final image IM_fin selected as a result of the measurement of the position of the inhomogeneity or defect 12 sought in the object 15.

[0157] Optionally, the method 100 may include a comparison of the initial image IM_in and the final image IM_fin based on the predetermined selection criterion. If no defect is actually present in the object 15 along the projection direction Ai, the final image IM_fin may not be improved compared to the initial image IM_in based on this selection criterion. The method 100 may then provide output information regarding the absence of a detected defect 12 along the projection direction A.

[0158] Advantageously, the method 100 is implemented for a plurality of projection directions Ai, generating a plurality of respective final images IM_fin. This arrangement amounts to virtually sliding the deconvolution kernel along different projection directions A; within the object 15 and thus not only positioning the defect in depth within the object according to a given projection direction A; but also selecting the projection direction A; that best reflects the position of the source 25.

[0159] The data processing system 20 can then provide as output of the process 100 the plurality of final images IM_fin, of improved quality compared to the initial image IM_in, and / or the plurality of respective reference distances Dref,fin as a result of the measurement of the position of the inhomogeneity or defect 12 sought in the object 15.

[0160] The method 100 thus makes it possible to locate an extended defect 12 in the object 15.

[0161] Alternatively, the method 100 may include selecting, by means of the data processing system 20, a single final image IM_fin_def from among the plurality of final images IM_fin, the selection being carried out for example on the basis of the predetermined selection criterion.

[0162] This single final image IM_fin_def of improved quality compared to the initial image IM_in, and / or the respective reference distances Dref,fin,def, and the position of the projection direction Ai>def are provided as a result of measuring the position of the inhomogeneity or defect 12 sought in the object 15.

[0163] Optionally, the method 100 may include the preliminary analysis of the initial image IM_in using a predetermined homogeneity criterion, and only if a defect 12 or an inhomogeneity 12 is detected at the end of the analysis, the implementation of steps ii) to v) of the method 100 on at least one ROI portion of the initial image IM_in including the detected defect 12 or inhomogeneity 12.

[0164] This situation is represented in [Fig.3].

[0165] The preliminary analysis can be carried out by the data processing system 20 or by another processor, remote or not.

[0166] The homogeneity criterion is, for example, a threshold value of contrast-to-noise ratio.

[0167] For example, if the contrast-to-noise ratio between a reference pixel or group of pixels and a pixel (or group of pixels) of interest exceeds the threshold value, it is determined that a defect 12 is detected in the IM_in image along the projection direction A; corresponding to the pixel (or group of pixels) of interest, the depth of this defect being to be determined. Said projection direction A; is then provided to the data processing system 20 for the implementation of steps ii) to v) of the method 100.

[0168] Optionally, steps ii) to v) are implemented not on the entire initial image IM_in but only on the portion of interest ROI of the initial image IM_in. This arrangement makes it possible to reduce the time and / or memory cost of steps ii) to v), or alternatively, at constant time and / or memory cost, to increase the quality of the improvement and / or localization of the defect 12 made possible by the method 100.

[0169] In particular, if the process is implemented only on the portion of interest ROI, the number Ni of reference distances Dref,i,j along the projection direction A; and / or, where applicable, the total number Q of source points and / or the total number of pixels PIX(m,p), may be increased.

[0170] The method 100 has been described in a transmission implementation, the radiation emitted by the source 25 being detected by the detector 30 after transmission through the object 15.

[0171] In an alternative embodiment, the radiation emitted by the source 25 is detected by the detector 30 after reflection on an internal or external surface of the object 15.

[0172] The method 100 according to the invention therefore makes it possible to improve the quality of the initial two-dimensional IM_in image of the object 15 in order to search for and position a defect 12 or an inhomogeneity 12 along a third dimension of the object 15 not preserved in the initial IM_in image, without prior knowledge of this defect or inhomogeneity, without requiring geometric deblurring or a plurality of initial images.

[0173] The method 100 can be implemented on an initial image IM_in acquired with an extended source 25 and possibly close to the object 15 without requiring geometric deblurring.

[0174] The determination of a deconvolution matrix on the basis of the geometric spreading function of the respective point PSFi,j for each reference distance Dref,i,j along the projection direction A; allows obtaining the respective deconvolved image IM_deconv,i,j which will be the sharpest image of the generated deconvolved images if and only if a defect 12 or an inhomogeneity 12 is actually present at the respective reference distance.

[0175] Modeling the source 25 by a plurality of source points allows working with an extended source 25.

[0176] Modeling the detection surface 30A by a grid of pixels PIX(m,p) and determining a respective simulated image density for each pixel PIX(m,p) allows us to determine a deconvolution matrix carrying information on the depth position of a defect 12.

[0177] The weighting of the respective image densities on the basis of a physical effect of radiation attenuation associated with the thickness of the object 15 traversed along the respective line SkPi,j and / or with the nature of the radiation emitted by the source, and / or on the basis of a geometric attenuation effect associated with the position of each respective source point Sk relative to the respective pixel PIX(m,p) makes it possible to make the respective deconvolved image more faithful to the expected image in the case where a defect 12 would actually be present at the respective reference distance Dref,i,j.

[0178] The use of a predetermined selection criterion chosen from a maximum of a contrast gradient and / or a contrast-to-noise ratio of the deconvolved image IM_deconv,i,j allows for an easy and efficient comparison of the deconvolved images IM_deconv with each other in order to determine the sharpest final image among these images.

[0179] Providing several final IM_fin images allows the positioning of the defect to be refined according to at least one of the two dimensions preserved in the initial IM_in image and / or to detect the shape and / or position of an extended defect 12.

[0180] The prior analysis of the initial IM_in image makes it possible to limit the consumption of the process by only proceeding with the improvement of this initial IM_in image if a defect 12 is probably present in the object 15. It also makes it possible to only proceed with the analysis of a ROI region of interest of the initial IM_in image, so that the time and / or memory cost of the process can be reduced and / or its accuracy can be improved.

[0181] The method 100 can be implemented on an initial image obtained by industrial radiography, particularly for detecting cracks in a pipe or in a steam generator tank. The method can also be implemented on an image obtained by medical radiography, with a suitable source 25.

Claims

1. Demands A method (100) for improving the quality of a two-dimensional image (IM_in) of a three-dimensional object (15) for the purpose of searching for and positioning a defect (12) or inhomogeneity (12) in the object (15), the method (100) comprising: i) providing as input an initial two-dimensional image (IM_in) of the object (15), the initial image (IM_in) being obtained by: - irradiation of the object (15) by means of a radiation source (25) emitting radiation towards the object (15) in at least one direction of propagation, and - detection by means of a radiation detector (30) comprising a two-dimensional detection surface (30A) of the radiation transmitted by the object (15); ii) the provision at input of a minimum distance (Dmin(A;)) and a maximum distance (Dmax(A;)) between the object (15) and the source (25) according to at least one projection direction (Ai); iii) for a plurality of reference distances (Dref,i,j) with respect to the source (25) along the projection direction (AO, the reference distances (Dref,i,j) being between the minimum (Dmin(Ai)) and maximum (Dmax(Ai)) distances, the determination (110) of at least one geometric spreading function of the respective point (PSFi,j) associated with a representative model of the radiation source (25) for at least one reference point positioned virtually in the object (15) at said reference distance (Dref,i,j) along the projection direction (AO; iv) for each reference distance (Dref,i,j), the generation (120) of a respective two-dimensional deconvolved image (IM_deconv,i,j) of the initial image by deconvolution of the initial image (IM_in) on the basis of the respective geometric point spreading function (PSFi,j); and v) as output, the provision: * of a final two-dimensional image (IM_fin) selected from the set of said deconvoluted images (IM_deconv,i,j) generated using at least one predetermined selection criterion which is a quantitative parameter for evaluating the sharpness of the deconvoluted images (IM_deconv,i,j), and optionally * of the respective reference distance (Dref,fin) associated with the final image (IM_fin) selected as a result of a measurement of the position of the inhomogeneity (12) or the defect (12) sought in the object (15).

2. Method (100) according to claim 1, wherein the source (25) is spatially extended and comprises a plurality of source points which can be separated taking into account a resolution and a position of the detector (30) relative to the source (25).

3. Method (100) according to claim 2, wherein no step of reducing or eliminating geometric blur is implemented.

4. Method (100) according to any one of claims 2 and 3, wherein each deconvolved image (IM_deconv,i,j) is generated by means of a respective deconvolution matrix which is determined from the respective geometric point spread function (PSFi,j) for each reference distance (Dref,i,j) along the projection direction (A;).

5. Method (100) according to claim 4, comprising the determination of the deconvolution matrix for each reference distance (Dref,i,j) according to the projection direction (Ai), this determination comprising: a) the modeling of the source (25) by means of a cloud of a plurality of discrete source points (Sk); b) the determination of a grid tiling at least a part of the detection surface (30A) and comprising a plurality of pixels (PIX(m,p));c) for each reference distance (Dref,i,j) along the projection direction (A,): c1) the positioning of a reference point (Pi,j) in the object at the reference distance (Dref,i,j) from the source (25) along the projection direction (Ai), c2) for each source point (Sk), the determination of a respective simulated image point (I_k,i,j) which is, if it exists, a point of intersection with the detection surface (30A) of a respective straight line (SkPi,j) passing through the source point (Sk) and the respective reference point (Pi,j); c3) for each pixel (PIX(m,p)) of the grid, the determination of a respective image density, including the calculation of a ratio between a total number of simulated image points (I_k,i,j) included in said pixel and a total number of simulated image points (I_k,i,j) included in the grid, each of the coefficients of the deconvolution matrix being determined from a respective image density.

6. Method (100) according to claim 5, wherein the determination of each coefficient of each deconvolution matrix includes the weighting of the respective image density on the basis of a physical radiation attenuation effect associated with a thickness of the object (15) traversed along the respective line (SkPi,j) and / or with a nature of the radiation emitted by the source, and / or on the basis of a geometric attenuation effect associated with a position of each respective source point (Sk) relative to the respective pixel (PIX(m,p)).

7. Method (100) according to any one of the preceding claims, wherein the predetermined selection criterion is chosen from a maximum of a contrast gradient and / or a contrast-to-noise ratio of the deconvolved image (IM_deconv,i,j).

8. Method (100) according to any one of the preceding claims, comprising supplying at output a plurality of respective final images (IM_fin) and / or reference distances (Dref,fin), each being obtained for a respective projection direction (A;).

9. A method (100) according to any one of the preceding claims, comprising analyzing the initial image (IM_in) using a predetermined homogeneity criterion, and only if a defect or inhomogeneity (12) is detected at the end of the analysis, carrying out steps ii) to v) on at least one region of interest (ROI) of the initial image (IM_in) comprising the detected defect (12) or inhomogeneity (12).

10. Data processing system (20) comprising means for carrying out the steps of the process according to any one of claims 1 to 9.

11. Data processing system (20) according to claim 10 comprising an image analysis module (20A) configured to implement steps iii), iv) and v) of the method according to any one of claims 1 to 9.

12. Device (10) for detecting an inhomogeneity or a defect (12) in an object (15) of interest, the device (10) comprising: - a data processing system (20) according to any one of claims 10 and 11, - a radiation source (25), and - a radiation detector (30) comprising a two-dimensional radiation detection surface.

13. Device (10) according to the preceding claim, wherein the source (25) is a penetrating radiation source selected from an Iridium 192 (192Ir) source, a Selenium 75 (75Se) source, a Cobalt 60 (60Co) source and an X-ray source.

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