Image processing device

Abstract

This effectively suppresses noise in images obtained by detecting X-rays. [Solution] The image processing apparatus 15 includes an acquisition unit 21 that acquires an image obtained by detecting X-rays as a first image, a calculation unit 22 that calculates a reference value for the pixel values ​​constituting the first image, a generation unit 23 that converts the values ​​of pixels in the first image that fall within a reference range set to include the reference value to a predetermined value and forms a second image, a conversion unit 24 that converts the first image to the spatial frequency domain to form first conversion information and converts the second image to the spatial frequency domain to form second conversion information, and a difference unit 25 that generates difference information between the first conversion information and the second conversion information.

Description

[Technical Field]

[0001] This invention relates to an image processing apparatus. [Background technology]

[0002] One method for measuring an object involves irradiating it with X-rays and detecting the diffracted X-rays. The detected diffracted X-rays allow for analysis of the object. Images obtained by detecting X-rays in this way are used in various fields, including medicine, manufacturing lines, baggage inspection, and stress measurement of objects.

[0003] For example, Patent Document 1 describes detecting diffraction rings to measure the stress on an object. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Patent No. 6600930 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] However, when obtaining an image by detecting X-rays, such as in diffraction X-ray images, outliers may be present in the pixels that make up the image. These outliers are caused, for example, by abnormalities in the detection elements that make up the detection surface. When such abnormal pixels occur, they can potentially affect the values ​​of surrounding pixels. In such cases, noise caused by the abnormal pixels may appear on the low-frequency side in the spatial frequency domain, making it difficult to suppress with a low-pass filter.

[0006] In view of the above issues, the present invention aims to provide an image processing apparatus that can effectively suppress noise in an image obtained by detecting X-rays. [Means for solving the problem]

[0007] To solve the above problems, the image processing apparatus according to the present invention comprises: an acquisition unit that acquires an image obtained by detecting X-rays as a first image; a calculation unit that calculates a reference value for the values ​​of the pixels constituting the first image; a generation unit that converts the values ​​of the pixels in the first image that fall within a reference range set to include the reference value to a predetermined value and forms a second image; a conversion unit that converts the first image into spatial frequency domain to form first conversion information and converts the second image into spatial frequency domain to form second conversion information; and a difference unit that generates difference information between the first conversion information and the second conversion information.

[0008] Furthermore, in the image processing apparatus, the generation unit converts the pixel values ​​that fall within the reference range to the lower limit of the pixel values ​​and generates the second image.

[0009] Furthermore, in the image processing apparatus, the calculation unit calculates the average value and standard deviation of the values ​​of each pixel in the first image as reference values, and the generation unit generates the second image using the range obtained by adding or subtracting a value based on the standard deviation from the average value as the reference range.

[0010] Furthermore, the image processing device includes an inverse conversion unit that converts the difference information from a spatial frequency domain image to a spatial domain image.

[0011] Furthermore, the image processing apparatus further includes an analysis unit that uses the image generated by the inverse transform unit with respect to the difference information as the target image and performs analysis using the target image, wherein the first image is a diffraction X-ray image detected by irradiating the target object with X-rays. [Effects of the Invention]

[0012] According to the image processing apparatus of the present invention, noise can be effectively suppressed in an image obtained by detecting X-rays. [Brief explanation of the drawing]

[0013] [Figure 1] This is a diagram showing an example of the configuration of an X-ray measuring apparatus according to an embodiment of the present invention. [Figure 2] This is a diagram showing an example of a first image. [Figure 3] This is a diagram showing an example of a second image. [Figure 4] This is a diagram showing an example of a first converted image. [Figure 5] This is a diagram showing an example of a second converted image. [Figure 6] This is a diagram showing an overview of low-pass filtering processing in the spatial frequency domain. [Figure 7] This is a diagram showing an example of the distribution of the amount of variation with respect to the rotation angle. [Figure 8] This is a flowchart showing an example of the processing flow by an image processing apparatus.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In order to facilitate understanding of the description, the same reference numerals are given to the same components in each drawing as much as possible, and duplicate descriptions are omitted as appropriate.

[0015] ===Embodiment=== <Overall Configuration> FIG. 1 is a diagram showing an example of the configuration of an X-ray measuring apparatus 1 according to the present embodiment. The X-ray measuring apparatus 1 irradiates an object 2 with X-rays and measures the X-rays diffracted by the object 2. The object 2 is a sample to be analyzed, and is, for example, an automotive part such as a gear, a crankshaft, a shaft, or other parts.

[0016] As shown in FIG. 1, the X-ray measuring apparatus 1 has a main body 10 and mainly includes an X-ray tube 11, a collimator 12, a substrate 13, and a detection unit 14. The X-ray measuring apparatus 1 also includes an image processing apparatus 15.

[0017] The X-ray tube 11 functions as an irradiation unit that generates X-rays and irradiates the generated X-rays toward the object 2. The X-ray tube 11 is made of a material such as glass or metal.

[0018] The collimator 12 has the function of adjusting the irradiation range of the X-rays generated by the X-ray tube 11. The collimator 12 is located below the X-ray tube 11 (on the substrate side) and extends toward the substrate 13 side. The X-rays generated by the X-ray tube 11 pass through the collimator 12 and are irradiated onto the object 2.

[0019] The substrate 13 is a plate-shaped member and is provided with a detection unit 14. The substrate 13 has a hole, and a collimator 12 extending from the tube 11 side protrudes from this hole toward the object 2 side.

[0020] The detection unit 14 is, for example, rectangular in shape and is provided on the substrate 13 on the side facing the object 2 (the side opposite to the light tube 11). The detection unit 14 and the substrate 13 may be provided in different positions, and their arrangement is not limited. The detection unit 14 detects X-rays diffracted from the object 2. The surface of the detection unit 14 that detects the diffracted X-rays becomes the detection surface. That is, the detection unit 14 has a detection surface. Specifically, the detection unit 14 detects the diffraction ring C, which is an annular diffraction pattern of the diffracted X-rays. The diffraction ring C is also called the Debye ring or Debye-Scherrer ring. The detection unit 14 has an imaging function for imaging the diffraction ring C on the detection surface. The detection unit 14 is configured, for example, as a chip equipped with an imaging function. For example, the detection unit 14 is a chip for an SOI (Silicon on Insulator) sensor. The detection unit 14 is not limited to an SOI sensor, but it is preferably suitable for digital processing. The detection unit 14 has a detection surface composed of multiple detection elements (the smallest units that make up the detection surface), and detects the intensity of X-rays diffracted by each detection element. For example, the detection surface is composed of multiple detection elements arranged vertically and horizontally. For example, the detection unit 14 is composed of two chips provided on either side of the collimator 12. The number of chips is not limited. Although a diffraction ring is given as an example above, this embodiment is not limited to this. For example, it can be applied to images in general that are generated by detecting X-rays, regardless of whether they are diffracted or transmitted. In other words, it goes without saying that the target includes not only diffraction rings but also images based on X-rays diffracted from a sample (diffracted X-ray images).

[0021] The image processing device 15 is an information processing device that performs various processes on the captured image (detection image) of the diffraction ring C. The image processing device 15 also processes the image to perform analysis on the object 2. The image processing device 15 includes, for example, a control device, a communication device, and a storage device. The control device mainly consists of a CPU (Central Processing Unit) and memory. In the control device, the CPU executes a predetermined program stored in memory or a storage device, thereby functioning in the various functional configurations described later. The communication device consists of a communication interface, etc. The storage device consists of a hard disk, etc., and stores various programs and information necessary for executing processing in the control device, as well as processing result information. The image processing device 15 may consist of a single information processing device or multiple information processing devices. The image processing device 15 may also include other components such as an operating device and a display device.

[0022] The program according to the embodiment causes a computer (processor) to execute each process and control of the image processing device 15 according to the embodiment. The recording medium according to the embodiment is a non-temporary recording medium (storage medium) that can be accessed by the computer on which the program according to the embodiment is recorded.

[0023] The processor includes circuits such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), and programmable logic devices (e.g., SPLD (Simple Programmable Logic Device), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array)). The processor realizes the functions according to the embodiment by, for example, reading and executing a program stored in a memory circuit or storage device. The memory circuit or storage device may be included in the processor. Alternatively, the memory circuit or storage device may be provided outside the processor.

[0024] <Functional configuration> Figure 1 shows a functional block illustrating an example of various functions in the image processing device 15. As shown in Figure 1, the image processing device 15 mainly comprises an acquisition unit 21, a calculation unit 22, a generation unit 23, a conversion unit 24, a difference unit 25, a filter unit 26, an inverse conversion unit 27, and an analysis unit 28.

[0025] The acquisition unit 21 acquires an image obtained by detecting X-rays. That is, the image is a diffracted X-ray image. In this embodiment, the image acquired by the acquisition unit 21 is called the "first image P1". The first image P1 is the raw data before each processing. The first image P1 is an image showing the diffraction ring C detected by the detection unit 14 when X-rays are irradiated onto the object 2. That is, the first image P1 is composed of the detection values ​​of a plurality of detection elements (detection components) that constitute the detection unit 14. That is, the respective detection value of each detection element that constitutes the detection unit 14 corresponds to the value of each pixel that constitutes the first image P1. For example, the first image P1 is a two-dimensional image showing the diffraction ring C. The acquisition unit 21 may acquire the detection result from the detection unit 14 as a two-dimensional image, or it may acquire the detection result for each row of one-dimensional detection elements and create a two-dimensional array. Alternatively, the detection result of each detection element may be acquired for each zero dimension and created a two-dimensional array.

[0026] Figure 2 shows an example of the first image P1. In Figure 2, images obtained from each of the two chips constituting the detection unit 14 are arranged side by side to form a single image. In the example in Figure 2, the upper half of the image Y1 corresponds to the image obtained from one of the chips constituting the detection unit 14, and the lower half of the image Y2 corresponds to the image obtained from the other chip constituting the detection unit 14. Note that the arrangement of the images is not limited to Figure 2. As shown in Figure 2, the X-rays irradiated onto the object 2 are detected as diffraction rings C by each detection element constituting the detection surface. The angle in the circumferential direction of the diffraction rings C is the α angle (diffraction ring central angle). The diffraction rings C are detected on each detection surface (which has multiple detection elements) of the two chips constituting the detection unit 14. Therefore, the diffraction rings C detected by each detection element are shown in the first image P1.

[0027] The detection unit 14 is composed of multiple detection elements, but there may be some detection elements whose detected values ​​are abnormal. These detection elements may be in a constantly abnormal state, or they may be in an abnormal state temporarily, such as due to temperature dependence. If an abnormal detection element exists in the detection unit 14, it may affect surrounding detection elements, causing the entire set of detection elements to become abnormal. Then, corresponding to each abnormal detection element, each pixel in the image will have an abnormal value. For example, Figure 2 shows a case where the set of pixels in region R1 has abnormal values. If there are pixels with abnormal values ​​in the first image P1, it may affect the accuracy of X-ray analysis using the first image P1. Therefore, various processes are performed on the first image P1.

[0028] The calculation unit 22 calculates the average value I of each pixel that makes up the first image P1. M The calculation unit 22 calculates the average value I for all the pixel values ​​of the first image P1 acquired by the acquisition unit 21. M The calculation unit 22 then calculates the standard deviation S corresponding to the first image P1. In this embodiment, the average value I of each pixel is used as the "reference value" for the value of each pixel. M One example is using this, but if it can be used as the basis for the value of each pixel, the average value I M It is not limited to this. For example, it is also possible to use the median value of each pixel as the reference value.

[0029] The generation unit 23 generates an image that highlights the abnormal areas in the first image P1. In this embodiment, the image generated by the generation unit 23 from the first image P1 is called the "second image P2".

[0030] Specifically, the generation unit 23 generates the average value I calculated in the calculation unit 22. M The reference range is set using the standard deviation S. The reference range is the calculated reference value (mean I M It is set as a range that includes the mean I. MIt is a range obtained by adding and subtracting a value based on the standard deviation S. The value based on the standard deviation S is a value obtained by multiplying the standard deviation S by a constant n. The constant n is preset as a constant for setting the reference range. The constant n is, for example, a natural number. The constant n is set to, for example, 3. That is, the reference range is I M ±nS (for example, I M ±3S).

[0031] And the generation unit 23 designates, for each pixel in the first image P1, a pixel having a value within the reference range as a "normal pixel" and a pixel having a value outside the reference range as an "abnormal pixel". Then, the generation unit 23 converts the value of a pixel having a value within the reference range into a predetermined value. That is, the generation unit 23 converts the value of a normal pixel in the first image P1 into a predetermined value. As a result, the value of each normal pixel in the first image P1 is unified to the predetermined value. Specifically, the predetermined value is set as the lower limit value of the pixel value. For example, the predetermined value is set to 0 (lower limit value).

[0032] In this way, the generation unit 23 converts the values of all normal pixels in the first image P1 to 0 to obtain a second image P2. That is, the second image P2 is an image in which the values of the normal pixels in the first image P1 are converted to 0 and the values of the abnormal pixels in the first image P1 are not converted and maintained. As a result, the second image P2 is an image in which the portions of the abnormal pixels in the first image P1 are emphasized.

[0033] FIG. 3 is a diagram showing an example of the second image P2. FIG. 3 shows an example of the second image P2 generated corresponding to the first image P1 of FIG. 2. Specifically, the generation unit 23 divides each pixel in the first image P1 of FIG. 2 into normal pixels and abnormal pixels and converts the values of the normal pixels to 0. By converting the first image P1 of FIG. 2 in this way, the diffraction ring C appearing in the first image P1 is converted to 0 as a normal pixel. On the other hand, the region R1 appearing in the first image P1 is maintained as a set of abnormal pixels.

[0034] This prepares the first image P1 acquired by the acquisition unit 21 and the second image P2 generated by the generation unit 23.

[0035] The transformation unit 24 performs a Fourier transform on both the first image P1 and the second image P2. The Fourier transform is, for example, a two-dimensional Fourier transform. The transformation unit 24 transforms the first image P1 into the spatial frequency domain. The spatial frequency domain is the space that represents what frequency components the image has. The information obtained by transforming the first image P1 into the spatial frequency domain is called "first transformation information F1". The first transformation information F1 is shown, for example, as an image (first transformation image). The transformation unit 24 also transforms the second image P2 into the spatial frequency domain. The information obtained by transforming the second image P2 into the spatial frequency domain is called "second transformation information F2". The second transformation information F2 is shown, for example, as an image (second transformation image). The first transformation image and the second transformation image are, for example, FFT images. The transformation unit 24 is not limited to a two-dimensional Fourier transform when performing a Fourier transform. For example, it may perform a one-dimensional Fourier transform (profile) multiple times (for example, twice).

[0036] Figure 4 shows an example of the first transformation information F1. Figure 4 is the first transformation information F1 corresponding to the first image P1 in Figure 2. Figure 5 shows an example of the second transformation information F2. Figure 5 is the second transformation information F2 corresponding to the second image P2 in Figure 3. In this way, an image in the spatial domain is transformed into the spatial frequency domain. In the examples in Figures 4 and 5, the frequency is shown by an axis relating to the horizontal frequency u and an axis relating to the vertical frequency v, and the spectrum corresponding to each frequency is shown. Note that in the spatial frequency domain, the frequency is lower towards the center of the two axes relating to the horizontal frequency u and the vertical frequency v, and the frequency is higher as you move outward from the center.

[0037] The difference unit 25 generates difference information between the first transformation information F1 and the second transformation information F2. The difference information is shown, for example, as an image (difference image). Specifically, the difference unit 25 subtracts the second transformation information F2 from the first transformation information F1 in the spatial frequency domain to obtain the difference information. As a result, components related to abnormal pixels are suppressed from the first transformation information F1.

[0038] The filter unit 26 performs low-pass filtering on the difference information in the spatial frequency domain. Low-pass filtering is a process that attenuates frequency ranges higher than a set threshold and transmits (maintains) frequency ranges lower than the threshold. Specifically, the filter unit 26 replaces frequency ranges exceeding the threshold in the spatial frequency domain with 0 (lower limit). For example, the threshold is set to the lower m% of the frequency of the difference information. For example, the threshold is set in advance as the lower 10%, etc. In other words, the lower frequency components of the lower m% of the frequency components included in the difference image are extracted.

[0039] Figure 6 shows an overview of the low-pass filter processing in the spatial frequency domain. In the spatial frequency domain, the center is in the low-frequency domain, so the filter unit 26 performs attenuation processing on the region R3 outside of the region R2 located at the center of the difference information. As a result, low-pass filter processing is performed on the difference information. Low-pass filter processing can suppress noise components that appear on the high-frequency side. For example, if there are abnormal pixels with abnormal values ​​that are completely different from the surrounding pixels, the influence can be suppressed by low-pass filter processing.

[0040] The inverse transform unit 27 performs an inverse Fourier transform on the difference information that has undergone low-pass filter processing. In other words, the inverse transform unit 27 performs a transformation from the spatial frequency domain to the spatial domain. As a result, the difference information in the spatial frequency domain is transformed into an image in the spatial domain. The image in the spatial domain transformed by the inverse transform unit 27 is called the "final image P3". The final image P3 is the spatial domain image corresponding to the first image P1.

[0041] Figure 7 shows an example of the final image P3. Specifically, Figure 7 shows an example of the final image P3 obtained by inverse Fourier transforming the difference information between the first transformation information F1 in Figure 4 and the second transformation information F2 in Figure 5. In Figure 7, the upper half of the image Z1 corresponds to image Y1, and the lower half of the image Z2 corresponds to image Y2. As described above, the difference processing in the spatial frequency domain suppresses the component related to the anomalous pixel from the first transformation information F1. Therefore, as shown in Figure 7, the final image P3 shows the diffraction ring C, while the influence of the anomalous pixel shown in region R1 of the first image P1 is suppressed. In addition, anomalous components appearing in the high-frequency domain are suppressed by low-pass filter processing.

[0042] The analysis unit 28 performs analysis using the final image P3 generated by the inverse Fourier transform as the target image. Specifically, the analysis unit 28 performs analysis on the object 2 based on the diffraction ring C shown in the target image. For example, the analysis unit 28 performs X-ray analysis to analyze the stress and full width at half maximum (hardness) of the object 2.

[0043] For example, the analysis unit 28 generates a diffraction profile for each α-angle for the diffraction ring C shown in the target image. The diffraction profile shows the radial intensity distribution in the direction of the α-angle, and for example, the vertical axis is shown as intensity and the horizontal axis is shown as the radial position relative to the center of the diffraction ring C. The diffraction profile shows that the intensity is high at the radial position where the diffraction ring C is detected and low at the position where the diffraction ring C is not detected. The diffraction profile is then approximated using the following equation (1).

[0044]

number

[0045] In equation (1), 2θ is the diffraction angle. That is, 2θ corresponds to the radial direction of the diffraction ring C in the diffraction profile. f(2θ) is the intensity corresponding to the diffraction angle. That is, f(2θ) corresponds to the intensity in the diffraction profile. A is the peak height. μ is the peak position. σ is the Gaussian width. a and b correspond to the slope and intercept of the line, respectively. That is, the first term on the right-hand side of equation (1) corresponds to the Gaussian approximation, and the second term and subsequent terms on the right-hand side of equation (1) correspond to the linear approximation.

[0046] The analysis unit 28 approximates the intensity distribution shown by the diffraction profile of a predetermined α angle in the diffraction ring C using equation (1) to obtain parameters such as μ (peak position) and σ (Gaussian width). Then, for example, the analysis unit 28 can analyze the stress from the change in μ (peak position) for each of the multiple α angles. Also, for example, the analysis unit 28 calculates the average value of each of the multiple α angles as σ A Therefore, the FWHM can be analyzed as the full width at half maximum using the following equation (2).

[0047]

number

[0048] In this way, the analysis unit 28 can perform X-ray analysis on the object 2. In this embodiment, the analysis unit 28 is shown as analyzing stress and full width at half maximum as one example, but it is not limited to analyzing stress and full width at half maximum as long as the parameters can be analyzed using the diffraction ring C.

[0049] <Processing flow> Figure 8 is a flowchart showing an example of the processing flow by the image processing device 15. Each of the following steps is performed after irradiating the object 2 with X-rays and detecting the diffraction ring C in the detection unit 14. Each of the following steps may be started automatically after detecting the diffraction ring C, or it may be started according to instructions from the user or the like. The order and content of each of the following steps can be changed as appropriate.

[0050] (Step SP10) The acquisition unit 21 acquires the first image P1 obtained by detecting the X-rays. Then, the process moves on to step SP11.

[0051] (Step SP11) The calculation unit 22 calculates the average value I corresponding to the first image P1. M The standard deviation S is calculated. Then, the process moves to step SP12.

[0052] (Step SP12) The generation unit 23 generates an average value I M Using the standard deviation S, a reference range is set, and each pixel of the first image P1 is classified into normal pixels and abnormal pixels. Then, the process moves to step SP13.

[0053] (Step SP13) The generation unit 23 converts the values ​​of normal pixels in the first image P1 to 0 (lower limit) and generates the second image P2. Then, the process proceeds to step SP14.

[0054] (Step SP14) The transformation unit 24 performs a Fourier transform on the first image P1 and the second image P2, respectively, to generate first transformation information F1 and second transformation information F2. Then, the process proceeds to step SP15.

[0055] (Step SP15) The difference unit 25 generates difference information between the first conversion information F1 and the second conversion information F2. Then, the process moves on to step SP16.

[0056] (Step SP16) The filter unit 26 performs a low-pass filter process on the difference information. Then, the process moves on to step SP17.

[0057] (Step SP17) The inverse transform unit 27 performs an inverse Fourier transform on the difference information that has undergone low-pass filter processing to generate the final image P3. Then, the process moves on to step SP18.

[0058] (Step SP18) The analysis unit 28 performs X-ray analysis using the final image P3 as the target image. For example, the stress and half-width corresponding to the object 2 are analyzed. Then the process is completed.

[0059] As described above, processing is performed to suppress the influence of anomalous pixels based on the first image P1 showing the diffraction ring C. Therefore, the analysis unit 28 can perform X-ray analysis using the diffraction ring C with the influence of anomalous pixels suppressed.

[0060] <Effects and Effects> In this embodiment, the image processing apparatus 15 includes an acquisition unit 21 that acquires an image obtained by detecting X-rays as a first image P1, and a reference value (average value I) of the pixel values ​​constituting the first image P1. M The system includes a calculation unit 22 that calculates the value of a first image P1, a generation unit 23 that converts the values ​​of pixels in the first image P1 that fall within a set reference range that includes the reference value to a predetermined value and generates the second image P2, a conversion unit 24 that converts the first image P1 to the spatial frequency domain to generate first conversion information F1 and converts the second image P2 to the spatial frequency domain to generate second conversion information F2, and a difference unit 25 that generates difference information between the first conversion information F1 and the second conversion information F2.

[0061] For example, an abnormal pixel may affect surrounding pixels. In such cases, it is difficult to isolate the abnormal pixel in terms of frequency components, and it is difficult to process it appropriately using a low-pass filter or the like. In contrast, with the above configuration, by taking a difference in the spatial frequency domain between the first image P1 and the second image P2, it is possible to effectively suppress the influence of the abnormal pixel that appeared in the first image P1. That is, noise caused by the abnormal pixel can be effectively suppressed in the first image P1 obtained by detecting X-rays. Therefore, by performing analysis using the difference information, it is possible to improve the accuracy of the analysis. In other words, the reliability of the analysis is improved. Furthermore, since various processing is performed software-wise based on the first image P1, processing can be performed at high speed, and the complexity of the hardware configuration and the increase in cost are suppressed.

[0062] Furthermore, in the image processing apparatus 15 according to this embodiment, the generation unit 23 converts the pixel values ​​that fall within the reference range to the lower limit of the pixel, and generates a second image P2.

[0063] This configuration makes it possible to highlight abnormal pixels in the first image P1 relative to normal pixels and display them as the second image P2. In other words, the second image P2 can be an image that specifically highlights abnormal pixels.

[0064] Furthermore, in the image processing apparatus 15 according to this embodiment, the calculation unit 22 calculates the average value I as a reference value for the value of each pixel in the first image P1. M The generation unit 23 calculates the mean I and the standard deviation S, and then generates the mean I M The second image P2 is generated using a reference range obtained by adding or subtracting a value based on the standard deviation S from the given value.

[0065] According to this configuration, the average value I M By using the standard deviation S, an appropriate reference range can be set. In other words, normal pixels and abnormal pixels can be effectively separated.

[0066] Furthermore, the image processing apparatus 15 according to this embodiment further includes an inverse conversion unit 27 that converts the difference information from the spatial frequency domain to an image in the spatial domain.

[0067] This configuration allows for a transformation from the spatial frequency domain to the spatial domain. In other words, it is possible to generate an image that corresponds to the first image P1 and has undergone difference processing and low-pass filter processing.

[0068] Furthermore, the image processing apparatus 15 according to this embodiment further includes an analysis unit 28 that performs analysis using the target image, with the image generated by the inverse transform unit 27 for the difference information being used as the target image, and the first image P1 is a diffracted X-ray image detected by irradiating the object 2 with X-rays.

[0069] This configuration allows for highly accurate X-ray analysis by performing analysis on the target image. Improved analysis accuracy leads to increased reliability.

[0070] <Variation> It should be noted that the present invention is not limited to the embodiments described above. That is, any design modifications made to the above-described examples by those skilled in the art are also included within the scope of the present invention, as long as they retain the features of the present invention. Furthermore, the elements of the above embodiments and the following modifications can be combined to the extent that it is technically possible, and any combination thereof is also included within the scope of the present invention, as long as it retains the features of the present invention.

[0071] Furthermore, although the above embodiment describes a case where the image processing device 15 is provided separately from the main unit 10, the image processing device 15 is not limited to being provided separately from the main unit 10, and may be mounted on the main unit 10. Also, the image processing device 15 is not limited to being provided together with the main unit 10 in the X-ray measuring device 1, and the image processing device 15 may be provided as a standalone (independent) device. In this case, the image processing device 15 acquires detection image information, such as RAW data, from a device that performs X-ray irradiation or detection, such as the main unit 10, and performs image processing.

[0072] Furthermore, in the above embodiment, the reference range is the average value I M As an example, we explained the case where AV ± nS is set using the standard deviation S, but the reference range is not limited to the above. For example, the reference range is the mean I M The reference range may be set to a range obtained by adding or subtracting a predetermined value from the mean I. M It is set to include the range. Also, the reference range is the mean I M Alternatively, you could use ±N% (where N can be set arbitrarily) based on the median.

[0073] Furthermore, in the above embodiment, the first transformation information F1 is an image obtained by Fourier transforming the first image P1, and the second transformation information F2 is an image obtained by Fourier transforming the second image P2, and each is shown as an FFT image. However, the embodiment is not limited to cases where the transformed result is an image. For example, the first transformation information F1 and the second transformation information F2 may each be data indicating the result of the Fourier transform.

[0074] Furthermore, although the above embodiment described an example where difference processing and low-pass filter processing are performed in the spatial frequency domain, the low-pass filter processing may be omitted. In addition, other processing besides difference processing and low-pass filter processing may be performed.

[0075] Furthermore, although the above embodiment describes a case in which the image processing device 15 processes an image obtained by detecting X-rays diffracted from the object 2, the invention is not limited to this. For example, the image processing device 15 can be applied to various images as long as they are images obtained by detecting X-rays. For example, the same processing as described above may be performed using a transmitted X-ray image.

[0076] Furthermore, while the above embodiment provided an example of how various functions are performed by the image processing device 15, the functions performed may be all or only some of them. In other words, some of the functions of the image processing device 15 may be omitted. For example, the example given was that the image processing device 15 performs all of the following functions: "the generation unit 23 sets the reference range for the conversion of the second image P2 to the standard deviation," "the generation unit 23 sets the predetermined value to be converted to the lower limit," "the filter unit 26 performs a low-pass filter," and "the inverse conversion unit 27." However, one or more functions may be arbitrarily selected and configured. In other words, the image processing device 15 is not limited to having all of the functional units: the acquisition unit 21, the calculation unit 22, the generation unit 23, the conversion unit 24, the difference unit 25, the filter unit 26, the inverse conversion unit 27, and the analysis unit 28. It may be configured to have only some of the functional units or some of the functions. [Explanation of Symbols]

[0077] 15: Image processing device 21: Acquisition section 22: Calculation Department 23: Generation part 24: Conversion section 25: Difference part I M :Average value F1: First Conversion Information F2: Second conversion information P1: First image P2: Second image

Claims

1. An acquisition unit that acquires an image obtained by detecting X-rays as the first image, A calculation unit that calculates a reference value for the pixel values ​​constituting the first image, A generation unit converts the values ​​of pixels in the first image that fall within a set reference range that includes the reference value to a predetermined value, thereby forming a second image. A conversion unit converts the first image into the spatial frequency domain to obtain first conversion information, and converts the second image into the spatial frequency domain to obtain second conversion information, A difference unit that generates difference information between the first conversion information and the second conversion information, An image processing device equipped with the following features.

2. The generation unit converts the pixel values ​​that fall within the reference range to the lower limit of the pixels and generates the second image. The image processing apparatus according to claim 1.

3. The calculation unit calculates the average value and standard deviation of the values ​​of each pixel in the first image, as the reference value. The generation unit generates the second image using the range obtained by adding or subtracting a value based on the standard deviation from the average value as the reference range. The image processing apparatus according to claim 1 or 2.

4. An inverse transform unit that converts the aforementioned difference information from a spatial frequency domain to a spatial domain image, The image processing apparatus according to claim 1 or 2, further comprising the above.

5. An analysis unit that uses the image generated by the inverse transform unit with respect to the difference information as the target image, and performs analysis using the target image, Furthermore, The first image above is a diffraction X-ray image detected by irradiating the object with X-rays. The image processing apparatus according to claim 4.

Images