Resolution improvement in dual energy

Abstract

In some embodiments, a method is provided for processing inspection data associated with a cargo irradiated by a plurality of N pulses of inspection radiation, the method comprising: obtaining the inspection data, the inspection data representing pixel intensity values ​​of an inspection image of the cargo, the inspection image of the cargo being generated as a result of a plurality of detectors detecting the plurality of N pulses emitted through the cargo, and the inspection data including data associated with a higher energy mode and data associated with a lower energy mode; generating a histogram having intervals as a first axis corresponding to pixel intensity values ​​HM associated with the higher energy mode and intervals as a second axis corresponding to pixel intensity values ​​LM associated with the lower energy mode, wherein generating the histogram includes, for each pixel (i, j) of the inspection image corresponding to each pulse i and each detector j of the plurality of detectors, merging the occurrence counts in the obtained inspection data such that: on the generated histogram, for each interval of the pixel intensity value LM, the interval corresponding to the most frequent interval of the pixel intensity value HM is selected; and a transformation table is generated by mapping each interval of the pixel intensity value LM to the selected interval of the pixel intensity value HM.

Description

Technical Field

[0001] This invention relates to, but is not limited to, methods for processing inspection data associated with goods irradiated by multiple inspection radiation pulses. The invention also relates to associated equipment and computer program products. Background Technology

[0002] Some accelerators have dual-energy modes, for example, for material identification. The accelerator is used to radiate cargo in both low-energy and high-energy modes. For a given cargo, the transmission levels of the modes are different, and the material of the cargo can be determined. Summary of the Invention

[0003] Aspects and embodiments of the invention are set forth in the appended claims. These and other aspects and embodiments of the invention are also described herein. Attached Figure Description

[0004] Embodiments of this disclosure will now be described by way of example only with reference to the accompanying drawings, in which:

[0005] Figure 1 A flowchart illustrating an example method according to this disclosure is shown schematically;

[0006] Figure 2 An example histogram according to this disclosure is schematically shown;

[0007] Figure 3 An example conversion table according to this disclosure is illustrated schematically; and

[0008] Figure 4 An inspection system including an analyzer is schematically shown, the analyzer being configured to at least partially perform the methods according to any aspect of this disclosure.

[0009] In the accompanying drawings, the same reference numerals are used to denote the same elements. Detailed Implementation

[0010] Overview

[0011] In some embodiments of this disclosure, a method is provided for processing inspection data obtained from cargo inspection using higher-energy-mode data and lower-energy-mode data. The method may include generating a histogram showing (i) the frequency of occurrence of pixels in the inspection data having a given intensity associated with higher-energy-mode data and (ii) the frequency of occurrence of pixels having a given intensity associated with lower-energy-mode data. The generated histogram may be used to associate each given intensity associated with lower-energy-mode data with a corresponding most frequent intensity associated with higher-energy-mode data. A transformation table mapping the associated intensities may be generated. The transformation table may be used to determine the transformed intensity corresponding to the higher-energy-mode data by transforming the intensity associated with the lower-energy-mode data.

[0012] Embodiments of this disclosure enable the use of converted intensities in combination with available intensities associated with higher energy mode data. Alternatively or additionally, embodiments of the invention enable the acquisition frequency to be effectively doubled during inspection. Alternatively or additionally, embodiments of the invention enable the image resolution to be doubled in the scanning direction during inspection. Alternatively or additionally, embodiments of the invention enable the improvement of the quality of the inspected image by using combined intensities. Alternatively or additionally, embodiments of the invention achieve faster scanning during inspection.

[0013] Alternatively or additionally, as already stated, embodiments of the present invention enable the generation of conversion tables using inspection data obtained from the inspection of the goods themselves. Alternatively or additionally, embodiments of this disclosure enable the conversion tables to remain accurate even when, for the goods, inspection radiation changes due to factors such as aging of the inspection system, variations in radiation source energy, detector replacement, or the addition of filters. Alternatively or additionally, embodiments of the present invention enable the avoidance of the need for calibration equipment of different materials and / or thicknesses to generate the conversion tables. Alternatively or additionally, embodiments of the present invention enable improvements in the reliability of the generated conversion tables.

[0014] Detailed description of exemplary embodiments

[0015] Figure 1 A flowchart illustrating an example method 100 according to this disclosure is shown schematically. Figure 1 Method 100 can be used to process inspection data associated with cargo irradiated by multiple (N) pulses of inspection radiation.

[0016] In some examples, inspection data can represent pixel intensity values ​​of an inspection image of the cargo. The inspection image can be generated as a result of multiple detectors detecting multiple (N) pulses emitted through the cargo. In examples using dual-energy modes, the inspection data includes data associated with the higher energy mode and data associated with the lower energy mode.

[0017] In some examples, data associated with higher energy modes includes data generated using pulses of the higher energy mode (e.g., by a radiation source that generates pulses to examine radiation), and data associated with lower energy modes includes data generated using pulses of the lower energy mode. In these examples, the radiation source can be configured to generate pulses with lower energies, such as essentially 4 MeV, and pulses with higher energies, such as essentially 6 MeV. Other energy values ​​can be envisioned for each mode.

[0018] In some examples, multiple (N) pulses comprise pairs of examined radiation, each pair consisting of a pulse of a higher energy mode and a pulse of a lower energy mode. In some examples, the radiation source can be configured to alternately generate pulses of lower and higher energies, i.e., a lower energy pulse follows a higher energy pulse, and vice versa. Alternatively or additionally, the radiation source can be configured to generate lower and higher energy pulses with a sequence different from the alternating sequence, i.e., two higher energy pulses following each other before and after a lower energy pulse. Other sequences of higher and lower energy pulses are conceivable.

[0019] Alternatively or additionally, in some examples, the data associated with the higher energy mode includes data generated using a detector that includes the higher energy mode, and the data associated with the lower energy mode includes data generated using a detector that includes the lower energy mode. In these examples, the radiation source may be configured to generate pulses with a given energy, such as energies included in the range of 4 MeV-6 MeV (other energy values ​​for the given energy can be envisioned), and each pulse with the given higher energy is detected by two detectors, i.e., the detector includes the higher energy mode and the detector includes both the higher energy mode and the lower energy mode.

[0020] Figure 1 Method 100 includes obtaining inspection data in S1. Method 100 also includes generating a histogram in S3.

[0021] Figure 2 An exemplary histogram 200 according to this disclosure is schematically shown. Figure 2 The histogram has:

[0022] As the first axis, it corresponds to the range (bin) of pixel intensity values ​​HM associated with higher energy modes.

[0023] As the second axis, it corresponds to the range of pixel intensity values ​​LM associated with the low-energy mode.

[0024] like Figure 2The right-hand side of the scale schematically represents that, in S2, the histogram 200 generated includes each pixel (i, j) corresponding to each pulse i and each detector j of the multiple detectors for the inspected image. The occurrence counts in the obtained inspection data are binning, such that:

[0025]

[0026] The thickness of the inspected goods is assumed to be constant relative to the pulse emission between pulses (i-1), i, and (i+1). As described above, HM(i,j) corresponds to the pixel intensity value of pixel (i,j) associated with a higher energy mode, such as a higher energy pulse i in some examples. Similarly, LM(i-1,j) and LM(i+1,j) correspond to the pixel intensity values ​​of pixels (i-1,j) and (i+1,j) associated with lower energy modes, such as lower energy pulses (i-1) or (i+1) in some examples.

[0027] expression:

[0028]

[0029] This corresponds to the average intensity associated with the two lower modes, and corrects for small thickness transitions.

[0030] In production Figure 2 After the histogram is generated, method 100 can proceed to S4 to select the interval corresponding to the most frequent interval of pixel intensity value HM for each interval of pixel intensity value LM on the generated histogram. Figure 2 The dashed line above represents step S4. On the resulting histogram 200, for the interval of pixel intensity value LM0, the interval HM0 corresponding to the most frequent interval of pixel intensity value HM is selected.

[0031] Figure 1 Method 100 also includes generating a transformation table in S5 by mapping each interval of pixel intensity value LM to a selected interval of pixel intensity value HM. Figure 3 Example conversion table 300 according to this disclosure is illustrated schematically. Figure 3 The conversion table 300 includes a column with pixel intensity values ​​LM and a corresponding column with mapped pixel intensity values ​​HM (=LM').

[0032] Embodiments of this disclosure enable the generation of conversion tables using inspection data obtained from the inspection of the goods themselves. Alternatively or additionally, as already stated, embodiments of this disclosure enable conversion tables to remain accurate even when inspection radiation changes for the goods, for example due to aging of the inspection system, variations in radiation source energy, detector replacement, filter addition, etc. Alternatively or additionally, embodiments of the invention enable the avoidance of the need for calibration equipment of different materials and / or thicknesses to generate conversion tables. Alternatively or additionally, embodiments of the invention enable improvements in the reliability of the resulting conversion tables.

[0033] like Figure 3 As described herein, the method allows the determined transformed pixel intensity value LM' to be included in the range interpolated in the generated transformation table 300. For example, for a pixel intensity value LM(x)301 included between LM1 and LM2, the transformed pixel intensity value LM'(x)303 can be interpolated using HM1 and HM2.

[0034] Figure 1 The method may also include, in S6, determining whether the intensity value is greater than a predetermined threshold for each pixel of the inspected image associated with the lower energy mode. The predetermined threshold may be substantially equal to, for example, 0.01. Other values ​​for the predetermined threshold are conceivable. The predetermined threshold may be set by the user of the inspection system on which the inspection of the goods is performed. In some examples, the predetermined threshold ensures that the intensity value associated with the lower energy mode is not so low as to be unusable, because, for example, a pulse with lower energy does not sufficiently penetrate the goods.

[0035] If the intensity value is determined to be greater than a predetermined threshold in S6, then Figure 1 Method 100 may include, in S7, using, for each pixel having an intensity value greater than a predetermined threshold, ... Figure 3 The generated transformation table 300 shown determines the transformed pixel intensity value LM' corresponding to the pixel intensity value HM.

[0036] If the intensity value is determined to be less than a predetermined threshold in S6, then Figure 1 Method 100 may include determining, in S8, an average pixel intensity value HM' corresponding to the pixel intensity value HM, such that:

[0037]

[0038] Figure 1 Method 100 may further include generating an enhanced inspection image in S9 using at least one of a pixel intensity value HM associated with a higher energy mode, a determined converted pixel intensity value LM', and / or a determined average pixel intensity value HM'.

[0039] Embodiments of this disclosure enable the use of converted intensities in combination with available intensities associated with higher energy mode data. Alternatively or additionally, embodiments of the invention enable the acquisition frequency to be effectively doubled during inspection. Alternatively or additionally, embodiments of the invention enable the image resolution to be doubled in the scanning direction during inspection. Alternatively or additionally, embodiments of the invention enable the improvement of the quality of the inspected image by using combined intensities. Alternatively or additionally, embodiments of the invention achieve faster scanning during inspection.

[0040] In some examples, the hyperbolic tangent function can be used, and it provides a smooth transition between the intended uses of LM' and HM' in the enhanced inspection image. The intended uses of LM' and HM' can be determined by the user of the inspection system. Other functions besides the hyperbolic tangent function are also conceivable.

[0041] The following explains an example of using the hyperbolic tangent function.

[0042] As described above, if in S6 it is determined that the intensity value associated with the LM mode is higher than a predetermined threshold, then there are sufficient intensity values ​​associated with the LM mode to use the conversion table. The conversion table can be used to determine LM'. If in S6 it is determined that the intensity value associated with the LM mode is lower than the predetermined threshold, then there are not sufficient intensity values ​​associated with the LM mode, and the conversion table does not need to be used. More intensity exists in the pixels corresponding to the higher mode HM. The average pixel intensity value HM' corresponding to the pixel intensity value HM as described above can be used.

[0043] Let T be a predetermined threshold. S can be a pixel intensity value associated with a low-energy mode. W can be a predetermined width of the transition zone between the predetermined use of LM' and the predetermined use of HM' in the enhanced inspection image. Therefore, the width W can be selected by the user of the inspection system on which the inspection of goods is performed. LM' is an intensity value calculated from a transformation table corresponding to S as described above, and HM' is the average value corresponding to S as described above.

[0044] For each pixel intensity value S in the obtained inspection image, the pixel value V used in the enhanced inspection image can be such that:

[0045]

[0046] When the pixel intensity value S is much lower than the threshold T, we obtain

[0047]

[0048] This allows HM' to be used for image enhancement.

[0049] When the pixel intensity value S is much higher than the threshold T, we obtain:

[0050]

[0051] This allows LM' to be used to enhance images.

[0052] The goods to be inspected can include different materials or different thicknesses, depending on the pulse emission. Alternatively or additionally, the user of the inspection system performing the inspection may only be interested in a portion of the goods. Alternatively or additionally, due to the angular characteristics of the inspection radiation pulse, the image of the goods can include different features. Figure 1 In method 100, obtaining data in S1 may also include an optional step: in S2, selecting at least one region associated with a portion of the image in the examined data.

[0053] The selection step in S2 may include selecting at least one region in the inspection data that is associated with a portion of the cargo, the portion of the cargo having a constant material for multiple (N) pulses. In cases where the cargo to be inspected includes different materials, at least one region associated with a portion of the image may be selected, such that a histogram can be generated for each region having a constant material.

[0054] Alternatively or additionally, for multiple N pulses, the portion of the image can be associated with a portion of the cargo having a constant thickness. In cases where the cargo to be inspected includes portions of varying thicknesses, at least one region can be selected to be associated with a portion of the image, allowing a histogram to be generated for each region having a constant thickness.

[0055] Alternatively or additionally, at least one region associated with a portion of the image can be selected, such that a histogram can be generated for each region of interest to the user of the inspection system.

[0056] Alternatively or additionally, the portion of the image may be associated with at least one of the following: the upper portion of the image, the middle portion of the image, and the lower portion of the image. In cases where the image of the cargo includes different features due to the angular characteristics of the pulses of radiation being examined, different histograms can be generated for different portions of the image.

[0057] In some examples, for instance, the inspection data may be incomplete because no pixels have a normalized signal between 0.01 and 0.02. In the example methods of this disclosure, obtaining inspection data may further include obtaining additional data. In some examples, the additional data may include inspection data representing pixel intensity values ​​of an additional inspection image of additional cargo, which is generated as a result of scanning the additional cargo by emitting multiple pulses through it. Alternatively or additionally, the additional data may include calibration data representing pixel intensity values ​​of a calibration image. The calibration image is generated as a result of scanning a calibration device by emitting N pulses through it.

[0058] In some instances, in order to estimate the most likely HM for a given LM from a histogram, the intervals of the histogram can be selected as explained below.

[0059] The interval size can be chosen to be small enough to determine the most probable HM accurately, and large enough to encompass a large number of pixels. In some instances, the interval size of the intensity range may depend on the number of pixels in the examined image within the intensity range. In other words, the interval size may be small for intensity ranges with a large number of image pixels in the examined image, and large elsewhere.

[0060] Figure 4 An inspection system 1 is shown, which includes an analyzer 5 configured to at least partially perform methods according to any aspect of this disclosure. Figure 4 In the middle, the goods 101 to be inspected are located in container 100. Figure 4 The inspection system 1 can be configured to generate inspection data in accordance with any aspect of this disclosure.

[0061] The analyzer 5 can be configured to receive inspection data from the system 1, for example, via a communication network 6 (which can be wired and / or wireless). The analyzer 5 typically includes at least a processor and memory to execute the example methods according to this disclosure.

[0062] The inspection system 1 is configured to inspect container 100. In some non-limiting examples, the inspection can be performed by emitting inspection radiation from an inspection radiation source through container 100 to a detector.

[0063] exist Figure 4 In the example shown, communication server 8 can be configured to communicate with system 1 and / or analyzer 5 via communication network 7 (which can be wired and / or wireless). In some examples, communication server 8 can be configured to provide a remote data management system. In some examples, server 8 may include a database. The database can be configured to store inspection data and / or other data for any aspect of this disclosure.

[0064] Similarly, analyzer 5 can be configured to store inspection data and / or additional data for any aspect of this disclosure.

[0065] Variations and modifications

[0066] It should be understood that the radiation source being examined may include other radiation sources, such as ionizing radiation sources (e.g., gamma rays or neutrons) as non-limiting examples. The radiation source being examined may also include sources that are not suitable for activation by a power source, such as radioactive sources (e.g., using Co60 or Cs137).

[0067] As one possibility, a computer program, computer program product, or computer-readable medium is provided, comprising computer program instructions to cause a programmable computer to perform any or more of the methods described herein. In exemplary embodiments, at least some portions of the activities associated with analyzer 5 herein may be implemented in software. It should be understood that, if desired, the software components of this disclosure may be implemented in ROM (read-only memory). If desired, the software components may typically be implemented in hardware using conventional techniques.

[0068] In some examples, components of analyzer 5 and / or communication network 6 and / or 7 may use dedicated applications and hardware.

[0069] As will be clear to those skilled in the art, server 8 and / or analyzer 5 should not be understood as a single entity, but rather as a physical and / or virtual device including at least a processor and memory, which may be included in one or more servers, which may be located in a single location or may be geographically dispersed to form a distributed network (such as a “server farm”, for example using wired or wireless technology).

[0070] In some examples, one or more memory elements (e.g., the memory of a database and / or processor) may store data used for the operations described herein. This includes memory elements capable of storing software, logic, code, or processor instructions that are executed to perform the activities described in this disclosure.

[0071] A processor can execute any type of instructions associated with data to perform the operations detailed herein. In one example, a processor can transition an element or item (e.g., data) from one state or thing to another. In another example, the activities outlined herein can be implemented with fixed logic or programmable logic (e.g., software / computer instructions executed by a processor), and the elements identified herein can be some type of programmable processor, programmable digital logic (e.g., field-programmable gate array (FPGA), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), ASIC including digital logic, software, code, electronic instructions, flash memory, optical disc, CD-ROM, DVD ROM, magnetic card or optical card, other types of machine-readable media suitable for storing electronic instructions, or any suitable combination thereof.

[0072] Communication networks 6 and 7 may form only one network. The data received by analyzer 5 can typically be received on the range of possible communication networks 6 and / or 7, which include at least: satellite-based communication networks; cable-based communication networks; telephone-based communication networks; mobile phone-based communication networks; Internet Protocol (IP) communication networks; and / or computer-based communication networks.

[0073] In some examples, communication networks 6 and / or 7 and / or analyzer 5 may include one or more networks. Networks may be provided in any form, including but not limited to local area networks (LANs), wireless local area networks (WLANs), virtual local area networks (VLANs), metropolitan area networks (MANs), wide area networks (WANs), virtual private networks (VPNs), intranets, extranets, any other suitable architecture or system, or any combination thereof that facilitates communication within the network.

[0074] Container 100 can be any type of container, such as a stand, vessel, or box. Therefore, by way of non-limiting example, container 100 can be a trailer and / or pallet (e.g., a pallet conforming to European standards, US standards, or any other standard) and / or a train wagon and / or vehicle (such as a truck, van, and / or car and / or train) tank and / or luggage compartment, and / or container 100 can be a "transport container" (e.g., a tank or ISO container or a non-ISO container or a unit loading device (ULD) container). Therefore, it should be understood that container 100 can be any type of container, and thus in some examples it can be a suitcase. The radiation source is configured to cause the material (typically steel) passing through the walls of container 100 to inspect cargo 101, for example, for the detection and / or identification of cargo 101.

[0075] System 1 is configured to cause an inspection of the entire container 100 (i.e., the entire container 100) or a portion of the container 100 (i.e., only a selected portion of the container is inspected, for example, typically when inspecting a vehicle, the vehicle compartment may not be inspected, but the rear portion of the vehicle is inspected) in inspection mode.

[0076] Inspection system 1 may be movable and transportable from one location to another (system 1 may include a motor vehicle). Alternatively or additionally, inspection system 1 may be stationary relative to the ground and cannot be moved.

[0077] The radiation source for inspection may include an X-ray generator. The energy of the X-rays can be between 100 keV and 15 MeV, and the dose can be between 2 mGy and 20 Gy (Gy). For mobile inspection systems, for steel penetration capabilities of, for example, between 40 mm and 400 mm (typically, for example, 300 mm (12 inches)), the power of the X-ray source can be, for example, between 100 keV and 9.0 MeV (typically, for example, 2 MeV, 3.5 MeV, 4 MeV, or 6 MeV). For mobile inspection systems, the dose can be, for example, between 20 mGy and 120 mGy. For static inspection systems, for example, for steel penetration capabilities of, for example, between 300 mm and 450 mm (typically, for example, 410 mm (16.1 inches)), the power of the X-ray source can be, for example, between 4 MeV and 10 MeV (typically, for example, 9 MeV). For static inspection systems, the dose can be 17 Gy.

[0078] In addition to other conventional electrical components, the detector may include radiation detection lines, such as X-ray detection lines. The detector may also include other types of detectors, such as optional gamma and / or neutron detectors (e.g., suitable for detecting the presence of radioactive gamma and / or neutron-emitting materials within container 100 simultaneously with X-ray inspection). For mobile inspection systems, the detector may also include an electro-hydraulic boom that can operate in a retracted position in transport mode and in the inspection position. The boom may be operated by a hydraulic actuator (e.g., a hydraulic cylinder). For static inspection systems, the detector may also include a structure and / or bench. The detection line may be mounted on the boom or structure and / or bench, facing a source on the opposite side of container 100.

[0079] To inspect container 100, system 1 may include motion generating equipment such that system 1 can be moved while container 100 is stationary (this mode is sometimes referred to as "scanning" mode). Alternatively or additionally, the motion generating equipment may cause container 100 to be moved while system 1 is stationary relative to the ground. In some embodiments, throughput (i.e., the number of containers 100 and / or images 10 per unit time) may be 20 to 30 images per hour. Alternatively or additionally, in "through" mode, system 1 does not include motion generating equipment, and the container moves relative to system 1 while system 1 is stationary relative to the ground. In embodiments, throughput in through mode may be higher than throughput in scan mode, and in the case of inspecting a passing train, throughput in through mode may be, for example, 50 to 200 images per hour, or even 50 to several thousand images per hour (e.g., throughput exceeding 1000 images per hour).

[0080] The above embodiments should be understood as illustrative examples, and other embodiments are contemplated. It should be understood that any feature described with respect to any embodiment may be used alone or in combination with other described features, and may also be used in combination with one or more features of any other embodiment, or in any combination with any other embodiment. Furthermore, equivalents and modifications not described above may be employed without departing from the scope of the invention as defined in the appended claims.

Claims

1. A method for processing inspection data associated with cargo irradiated by a plurality of pulses of inspection radiation, wherein the plurality of pulses comprises inspection radiation binaries, each inspection radiation binary comprising a higher energy mode pulse and a lower energy mode pulse with energy lower than the higher energy mode pulse, the method comprising: Obtain the inspection data. The inspection data represents the pixel intensity values ​​of an inspection image of the cargo, which is generated as a result of multiple detectors detecting multiple pulses emitted through the cargo. The inspection data includes: data associated with higher energy modes and data associated with lower energy modes; A histogram is generated, the histogram having an interval as a first axis corresponding to the pixel intensity value HM associated with the higher energy mode and an interval as a second axis corresponding to the pixel intensity value LM associated with the lower energy mode. Generating the histogram includes: for each pixel (i, j) of the inspected image corresponding to each pulse i and each detector j in the plurality of detectors, merging the occurrence counts in the obtained inspection data, such that: { }； On the generated histogram, for each interval of the pixel intensity value LM, the interval corresponding to the most frequent interval of the pixel intensity value HM is selected; and A transformation table is generated by mapping each interval of the pixel intensity value LM to a selected interval of the pixel intensity value HM.

2. The method according to claim 1, further comprising: For each pixel of the inspected image associated with the lower energy mode, determine whether the intensity value is greater than a predetermined threshold; as well as For each pixel having an intensity value greater than the predetermined threshold, the pixel intensity value LM' corresponding to the transformation of the pixel intensity value HM is determined using the generated transformation table.

3. The method according to claim 2, further comprising: For each pixel having an intensity value less than the predetermined threshold, determine an average pixel intensity value HM' corresponding to the pixel intensity value HM, such that:

4. The method according to claim 2 or 3, wherein determining the converted pixel intensity value LM' comprises: The mapped intervals are interpolated in the generated transformation table.

5. The method according to claim 1, wherein obtaining the inspection data further comprises: Select at least one region in the inspection data that is associated with a portion of the image.

6. The method of claim 5, wherein the portion of the image is associated with at least one of the following: the upper portion of the image, the middle portion of the image, and the lower portion of the image.

7. The method of claim 5, wherein the portion of the image is associated with a portion of the cargo having a constant thickness for the plurality of pulses.

8. The method of claim 5, wherein obtaining the inspection data further comprises: Select at least one region from the inspection data that is associated with a portion of the cargo that has a constant material for the plurality of pulses.

9. The method according to claim 1, further comprising: The interval size for the intensity range is selected based on the number of pixels in the inspected image within the intensity range.

10. The method of claim 1, wherein obtaining the inspection data further comprises obtaining additional data, said additional data including at least one of the following: Inspection data representing pixel intensity values ​​of additional inspection images of the additional cargo, which are generated as a result of scanning the additional cargo by emitting multiple pulses through it, and / or Calibration data representing pixel intensity values ​​of a calibration image, which is generated as a result of scanning the calibration device by emitting multiple pulses through it.

11. The method of claim 1, wherein: The data associated with the higher energy mode includes data generated using pulses from the higher energy mode, and The data associated with the lower energy mode includes data generated using pulses from the lower energy mode.

12. The method of claim 1, wherein: The data associated with the higher energy mode includes: data generated using a detector that includes the higher energy mode, and The data associated with the lower energy mode includes data generated using a detector that includes the lower energy mode.

13. The method of claim 1, further comprising: For each pixel of the inspected image associated with the lower energy mode, determine whether the intensity value is greater than a predetermined threshold; as well as For each pixel with an intensity value greater than the predetermined threshold, the generated transformation table is used to determine the pixel intensity value LM' corresponding to the transformation of the pixel intensity value HM, and It also includes using the following to generate enhanced examination images: The pixel intensity value HM associated with the higher energy mode; and The determined pixel intensity value LM' and / or the determined average pixel intensity value HM'.

14. The method of claim 13, further comprising: For each pixel having an intensity value less than the predetermined threshold, determine an average pixel intensity value HM' corresponding to the pixel intensity value HM, such that: ， And generating the enhanced inspection image includes: for each pixel intensity value S associated with the lower energy mode LM in the obtained inspection image, using the intensity value V in the enhanced inspection image, such that: Where: T is the predetermined threshold. LM' is the converted pixel intensity value corresponding to S, calculated from the conversion table. HM' is the average pixel intensity value corresponding to S, and W is the predetermined width of the transition area between the predetermined use of LM' and the predetermined use of HM'.

15. An analyzer comprising: processor; as well as A memory storing instructions, which, when executed by the processor, enable the processor to perform a method for processing inspection data associated with cargo irradiated by a plurality of pulses of inspection radiation, wherein the plurality of pulses comprises inspection radiation binaries, each inspection radiation binary comprising a higher energy mode pulse and a lower energy mode pulse with energy lower than the higher energy mode pulse, the method comprising: Obtain the inspection data. The inspection data represents the pixel intensity values ​​of an inspection image of the cargo, which is generated as a result of multiple detectors detecting multiple pulses emitted through the cargo. The inspection data includes: data associated with higher energy modes and data associated with lower energy modes; A histogram is generated, the histogram having an interval as a first axis corresponding to the pixel intensity value HM associated with the higher energy mode and an interval as a second axis corresponding to the pixel intensity value LM associated with the lower energy mode. Generating the histogram includes: for each pixel (i, j) of the inspected image corresponding to each pulse i and each detector j in the plurality of detectors, merging the occurrence counts in the obtained inspection data, such that: { }； On the generated histogram, for each interval of the pixel intensity value LM, the interval corresponding to the most frequent interval of the pixel intensity value HM is selected; and A transformation table is generated by mapping each interval of the pixel intensity value LM to a selected interval of the pixel intensity value HM.

16. The analyzer of claim 15, wherein the method is the method of any one of claims 2 to 14.

17. A computer program or computer program product comprising instructions that, when executed by a processor, enable the processor to perform the method according to any one of claims 1 to 14.

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