Patent Description:
It may be difficult for a user to detect hidden objects, such as weapons or dangerous material, in inspection images, particularly when the images are corrupted by noise. This is particularly true when the inspection images are generated by an inspection system with small radiation (such as x-ray) doses, as the SNR (Signal-to-Noise Ratio) may be small.

Zooming in the corrupted images usually does not help the user in detecting the hidden objects, because the noise corrupting the images is also enlarged by the zooming. Aspects of the present invention address some of the above issues. [insert page 1a].

Aspects and embodiments of the invention are set out in the appended claims. These and other aspects and embodiments of the invention are also described herein.

<FIG> is a flowchart that illustrates an example method of denoising one or more inspection images, or at least a part of at least one inspection image.

<FIG> very diagrammatically illustrates an apparatus <NUM> comprising a denoiser and/or zoom <NUM> configured to implement at least some of the steps of the method illustrated by <FIG>.

In some examples, the inspection images may be generated by an inspection system <NUM> configured to inspect one or more containers which comprise a load. An example of the inspection system will be described in greater detail below with reference to <FIG>.

As described in greater detail below, the inspection system <NUM> may be configured to inspect the container by transmission, through the container, of inspection radiation (e.g. x-ray) and may be configured to detect the transmitted radiation on an inspection radiation receiver comprising a plurality of detectors. In some examples, the inspection radiation may be transmitted in successive pulses of radiation. As will be apparent in the present disclosure, the inspection radiation may have an angular divergence from an inspection radiation source to the inspection radiation receiver.

The inspection system <NUM> may have certain instabilities, e.g. the inspection radiation source may not be stable over time and may experience an evolution in the intensity of the inspection radiation during the inspection and/or in the spectral properties of the inspection radiation.

The noise may be important for inspection carried out with small doses of radiation (such as from 2mGy to 60mGy, typically 20mGy for example). An example of an inspection image <NUM> having a plurality of pixels <NUM> is illustrated by <FIG>.

<FIG> shows that the inspection image <NUM> may be corrupted by a Poisson-Gaussian noise.

A graph of a standard deviation (i.e. the square root of a variance) of the noise of <FIG>, as a function of the detectors associated with the pixels <NUM> in the inspection image <NUM>, is illustrated by <FIG>. The graph of <FIG> shows a trough <NUM> in the curve. The trough <NUM> of <FIG> illustrates that the variance (the standard deviation of <FIG>, squared) of the noise is non-constant in the detectors, and thus in the image.

The inspection image <NUM> generated by the inspection system <NUM> is thus fundamentally different compared to a natural image (such as an image captured by known CCD and/or CMOS cameras), as the natural image may be corrupted with a noise having a constant variance in the natural image.

The method illustrated by <FIG> may thus comprise, at S2, denoising the received inspection image by applying, to the inspection image, a variance-stabilizing transformation for transforming the variance of the noise into a constant variance in the image. <FIG> illustrates an example of a transformed image obtained by applying the transformation. <FIG> shows a graph of a standard deviation (i.e. the square root of the variance) of the noise corrupting the transformed image illustrated by <FIG>, as a function of the detectors associated with pixels in the transformed image. <FIG> shows that the standard deviation may be approximated by a constant C (e.g. equal to <NUM>), and the variance may thus also be approximated by a constant (also e.g. equal to <NUM>).

In some examples, the variance-stabilizing transformation may be based on a descriptor associated with the angular divergence of the inspection radiation and the Poisson-Gaussian noise. In some examples the descriptor may be based on a predetermined set of parameters.

In the example of <FIG>, the denoiser and/or zoom <NUM> comprises a processor <NUM>, a memory <NUM>, a first communication interface <NUM> and a second communication interface <NUM>, and a Graphical User Interface <NUM> comprising a display <NUM> for displaying the inspection images <NUM> to the user. In some examples, the denoising and/or zooming may be performed, at least partly, by the denoiser and/or zoom <NUM>, e.g. by the processor <NUM> of the denoiser and/or zoom <NUM>.

The memory <NUM> is configured to store data, for example for use by the processor <NUM>. The memory <NUM> may comprise a first database server <NUM>.

In <FIG>, the denoiser and/or zoom <NUM> is configured to communicate with one or more inspection systems <NUM>, via the interface <NUM> and a first link <NUM> of the apparatus <NUM>, the link <NUM> being located between the interface <NUM> and each one of the systems <NUM>. The link <NUM> may comprise a communication network (wired and/or wireless).

In some examples, the denoiser and/or zoom <NUM> of <FIG> may be configured to receive, at S1 of <FIG>, the one or more inspection images <NUM> generated by the systems <NUM>, from one or more systems <NUM>, for example over the link <NUM>. The received images <NUM> may be stored in the memory <NUM>. In some examples, the memory <NUM> may also be configured to store data (e.g. control data) received from the systems <NUM> over the link <NUM>. The database server <NUM> may be configured to store parameters and/or instructions and/or files, such as files corresponding to the inspection images or the parameters corresponding to the descriptor <NUM>, for use by the denoiser and/or zoom <NUM>.

In the example illustrated by <FIG>, the apparatus <NUM> may further comprise a communication server <NUM>, configured to communicate, via a second link <NUM>, with some of the systems <NUM> and/or the denoiser and/or zoom <NUM> (via the interface <NUM>). The link <NUM> may comprise a communication network (wired and/or wireless). In some examples, the communication server <NUM> may be configured to provide a remote data management system to the systems <NUM> and/or the denoiser and/or zoom <NUM>. In some examples the server <NUM> may comprise a second database server <NUM>. The second database server <NUM> may be configured to store parameters and/or instructions and/or files, such as the parameters corresponding to the descriptor <NUM>, for use by the systems <NUM> and/or the denoiser and/or zoom <NUM>.

<FIG> shows an example of the system <NUM> (sometimes referred to as the "radiography equipment" hereinafter), described with reference to an orthonormal reference OXYZ, axis OY being the ascending vertical, a median plane XOY being vertical, and plane XOZ being horizontal.

The equipment <NUM> illustrated by <FIG> comprises:.

The equipment <NUM> of <FIG> is designed for the radiography of the container <NUM> which comprises the load <NUM>.

The inspection radiation of <FIG> comprises a part <NUM> and a part <NUM>. The inspection radiation shown in <FIG> has the angular divergence ω from the inspection radiation source <NUM> to the inspection radiation receiver (<NUM>, <NUM>).

The source <NUM> shown in <FIG> comprises a device for producing and accelerating an electron beam <NUM>. The source <NUM> may further comprise a target <NUM> for the electron beam, comprising a metal (such as tungsten and copper), so as to generate the divergent part <NUM> of the radiation from a focal point F. The photons of the part <NUM> are for example generated by the so-called braking radiation effect (or "Bremsstrahlung").

The detectors <NUM> are positioned in the extension of the part <NUM> of the radiation. They delimit, with the reference block <NUM>, an intermediate space <NUM> for the passage of the container <NUM> to be inspected.

With reference to <FIG>, the detectors <NUM> are numbered by reference <NUM>(i), with <NUM>≤(i)≤n, with n a number of detectors <NUM> in the plurality of detectors <NUM>, with e.g. n=<NUM>. The detectors <NUM>(i) are individually electrically connected to the control and signal processing device <NUM>. The detectors <NUM>(i) of <FIG> are adjacent to one another, and extend along a broken line forming a line array <NUM> of detectors <NUM>(i), situated substantially in the median plane XOY.

Each detector <NUM> of <FIG> is capable of receiving an individual angular sector of the part <NUM> of the radiation after it has successively passed through the reference block <NUM> and the intermediate space <NUM> (optionally occupied by the container <NUM> to be inspected). The line array <NUM> of detectors <NUM>(i) of <FIG> only covers a section of the container <NUM> to be inspected. Therefore, during an inspection, the container <NUM> and/or the array <NUM> is moved in the direction OZ (sometimes referred to the "direction of scan" herein) to obtained the 2D inspection image <NUM> of the container <NUM> (as illustrated e.g. by <FIG>). During the inspection, the radiation <NUM> thus irradiates successive sections of the container <NUM>. Hereinafter, the successive sections are numbered using an index k. The control and signal processing device <NUM> illustrated by <FIG> is configured to form the inspection image <NUM> (e.g. illustrated by <FIG>) based on signals received from the detectors <NUM>. As illustrated by <FIG>, a pixel <NUM>(i) of the inspection image <NUM> is thus associated with the detector <NUM>(i) of the receiver.

Because of the characteristics of the system <NUM> (for example the characteristics of the source <NUM>), the inspection image <NUM> of <FIG> is corrupted by the Poisson-Gaussian noise and the variance of the noise is non-constant in the plurality of pixels <NUM> of the image <NUM>.

In some examples, the value z(i) of the pixel <NUM>(i) of the inspection image <NUM> associated with the detector <NUM>(i) of the receiver can be defined as: <MAT> where:.

At S2 illustrated by <FIG>, the variance-stabilizing transformation applied to the inspection image are based on the descriptor <NUM> illustrated by <FIG> and associated with the angular divergence ω of the inspection radiation (<NUM>,<NUM>) of <FIG> and the Poisson-Gaussian noise corrupting the image <NUM> of <FIG>. The descriptor <NUM> is associated with each pixel <NUM> of the plurality of pixels of the inspection image <NUM>. The descriptor <NUM> takes into account the Poisson-Gaussian noise of the inspection image <NUM> generated by the inspection system <NUM>, the variance of which is non-homogeneous in the plurality of pixels, contrary to the variance in the noise corrupting natural images (such as images captured by known CCD and/or CMOS cameras). In such an example, the descriptor <NUM> may take into account at least one or more of the following instabilities of the inspection system:.

The line array <NUM> of detectors <NUM>(i) of <FIG> is displaced in the direction of scan OZ during the inspection. In such an example, and as illustrated by <FIG>, a signal <NUM> associated with the detector <NUM>(i) in the array <NUM> of detectors <NUM> in the direction of scan OZ is assumed to have the same signal properties in the inspection image. In such an example, the descriptor <NUM> associated with each pixel <NUM> of the plurality of pixels is simplified compared to the example where the descriptor <NUM> may be associated with each pixel <NUM> of the plurality of pixels of the inspection image <NUM>. In the example illustrated in <FIG>, the descriptor <NUM> is associated with each pixel <NUM><NUM>, <NUM><NUM>,. , <NUM>(i),. , 13n, respectively associated with the n detectors <NUM>(i). In such an example, the descriptor <NUM> illustrated by <FIG> comprises the predetermined set of parameters comprising an nxp matrix, with n the number of detectors <NUM> in the plurality of detectors, and p a number of parameters (sometimes referred to as "local parameters") of the variance-stabilizing transformation. In some examples, the parameters may comprise three parameters (p=<NUM> in <FIG>), and the parameters may comprise the parameters (α(i),µ(i),σ(i)) associated with (E1) above.

In some examples, the method illustrated by <FIG> comprises, at S2, applying an Anscombe transformation f, based on the predetermined set of three parameters (α(i),µ(i),σ(i)).

In some examples, the Anscombe transformation f may be defined by, for a value z(i) of a pixel of the inspection image associated with a detector <NUM>(i) of the receiver: <MAT> for <MAT>.

An example of an Anscombe transformation of S2 is described in document "<NPL>.

After the transformation has been applied, the transformed image is corrupted by an additive Gaussian noise.

In some examples, the method of <FIG> comprises determining the descriptor <NUM> (and e.g. the parameters (α(i),µ(i),σ(i))) during a calibration step S0, for a given inspection system <NUM>, in order to take into account the characteristics of the system (including the radiation source).

Below are described some non-limiting example methods to determine the parameters (α(i),µ(i),σ(i)) at S0 of the method illustrated at <FIG>.

In a first example illustrated with reference to <FIG>, the descriptor <NUM> is determined, for the given inspection system, based on a series of calibration images generated by the given system.

<FIG> shows one example of a calibration image <NUM>.

In some examples, a predetermined set of three parameters (α1(i),µ1(i),σ1(i)) of the descriptor may be determined, for the given inspection system, based on the series of calibration images generated by the given system. In some examples, the determination of the predetermined set of three parameters (α1(i),µ1(i),σ1(i)) of the descriptor may comprise:.

The determination is explained below in greater detail.

Like the inspection images <NUM> generated by the given system <NUM>, the calibration inspection image <NUM> shown in <FIG> comprises a plurality of pixels <NUM> corrupted by the Poisson-Gaussian noise, and the variance of the noise is non-constant in the plurality of pixels <NUM>.

During the calibration step, the standard deviation σ(i) and the mean µ(i) of the noise are calculated for each of the pixels <NUM>(i) illustrated in <FIG>. In the example illustrated by <FIG>, each of the pixels <NUM>(i) corresponds to a zone of homogeneous level of grey in the calibration image <NUM>. The standard deviation σ(i) and the mean µ(i) are calculated for example for each of the pixels <NUM>(i) shown with a spot in <FIG>, e.g. for <NUM> pixels <NUM>(i) of the image <NUM> of <FIG>.

In the example illustrated in <FIG>, the standard deviation σ(i) is represented as a point <NUM>(i) as a function of the mean µ(i) of the noise in the pixel <NUM>(i) on a graph <NUM>, for each of the pixels <NUM>(i). In the example of <FIG>, for example <NUM> points <NUM>(i) corresponding to the <NUM> pixels <NUM>(i) of the image <NUM> of <FIG> are represented in the graph <NUM> of <FIG>.

In some examples, the calculating and the representing may be performed for a series of calibration images <NUM> (for example a few tens, a hundred or a few hundreds of calibration images <NUM>, depending on available calibration images and/or on the desired precision), the calibration images <NUM> being generated by the given system <NUM>, in order to obtain the cloud <NUM> of points of <FIG>.

In some examples, the cloud of points <NUM> may be approximated first by a function <NUM> (using a fitting algorithm). In some examples, the function <NUM> may be approximated by a straight line <NUM> (using a fitting algorithm), such as:.

In some examples the cloud may be approximated directly by the straight line <NUM>, i.e. without the use of the approximation by the function <NUM>.

It should be understood that once the three parameters (α1(i),µ1(i),σ1(i)) are determined, Equation (E1) above may be applied based on the parameters (α1(i),µ1(i),σ1(i)) to the inspection image <NUM> (e.g. of Figure 6A) generated by the given system <NUM> such that: <MAT> for <MAT> and <MAT> for <MAT>.

It should be understood that in the above example, the respective parameters (α1(i),µ1(i),σ1(i)) are constant over n, i.e. all the lines of the descriptor <NUM> of <FIG> are equal.

Below is described a second non-limiting example method to determine the parameters (α(i),µ(i),σ(i)) at S0 of the method illustrated at <FIG>.

In the second example illustrated in <FIG>, the method may comprise determining the descriptor (e.g. the parameters (α(i),µ(i),σ(i))) during a pre-processing step, for pre-processing images (such as raw images) associated with the detectors <NUM>(i), i.e. generated by the system <NUM>. In some examples, as explained in greater detail below, the pre-processing step may comprise determining at least one of:.

In some examples, the reference may take into account at least one of.

As shown in (E1), σ(i) may be associated with the Gaussian noise component of the Poisson-Gaussian noise corrupting the inspection image. However, because of the characteristics of the system (including the source), a conventional definition of the standard deviation σ(i) cannot be used (e.g. it would create strip artifacts in low signal parts of the transformed image). In some examples, σ(i) may be determined by: <MAT> with.

In some examples, the offset image may comprise e.g. an "open flame" image, i.e. an image generated by the inspection radiation when the container <NUM> is not present in the intermediate space <NUM> of <FIG>. In the example of <FIG>, the offset image may comprise a zone <NUM> (sometimes referred to as "zone of gain") in an inspection image <NUM>, for example before the container <NUM> passes in the median plane XOY.

The zone <NUM> illustrated by <FIG> has a width W in the direction of scan OZ created by the scan movement during the inspection. The width W corresponds to k columns of pixels <NUM>, corresponding to successive sections of the image <NUM>.

Below is described an example method for obtaining σoff(i).

In some examples, an offset Offset(i) for the detector <NUM>(i) may be determined by: <MAT> with loffset(i,j) being a value of a pixel signal of the zone <NUM> for detector <NUM>(i), for column j of the zone <NUM>.

In some examples, a variance VarOffset(i) of Offset(i) may be determined by: <MAT>.

In some examples, the standard deviation σoff(i) may be determined by: <MAT>.

Below is described an example method for obtaining Gain(i).

Gain(i) may be determined by the time mean of an offset-corrected gain image generated by the system. In some examples, Gain(i) may be determined by: <MAT> with Igain(i,j) such that: <MAT>.

Below is described an example method for obtaining µ(i).

µ(i) may be associated with the Gaussian noise component of the Poisson-Gaussian noise, and, after the transformation has been applied, the transformed image is corrupted by an additive Gaussian noise, and in some examples, µ(i) may be assumed to be null: <MAT>.

(E1) shown above shows that α(i) may be associated with the Poisson noise component of the Poisson-Gaussian noise.

In some examples, α(i) may be associated with an approximation of an inverse of a number of photons emitted by the source and received by the detector <NUM>(i). In some examples, α(i) may be determined by: <MAT> with SNR(i), a Signal-to-Noise ratio in the pixel (i) associated with the detector <NUM>(i) of the receiver.

<FIG> is a graph showing two examples (referred to as "High" and "Low") of the Signal-to-Noise Ratio (SNR) as a function of the detectors in the system. <FIG> is a graph showing an example of a squared Signal-to-Noise Ratio (SNR<NUM>) associated with the detectors as a function of the angle θ from which the detectors are seen by the source (see <FIG>). <FIG> is a graph showing a number of photons emitted by the source as a function of the detectors in the system.

<FIG> show an angular signature of the angular divergence ω of the source.

<FIG> show that functions representative of the angular signature of the angular divergence may be determined, for the given inspection system, to take into account the geometry of the system, the spectrum of the inspection radiation and the spectral sensitivity of the detectors <NUM> (and for the spectral sensitivity of the intermediate sensors <NUM> of the reference block <NUM>). The representative functions may be determined either experimentally or through simulation calculations.

In the example of <FIG>, the squared Signal-to-Noise Ratio (SNR<NUM>) may be approximated by a Lorentz model LM, such that: <MAT> with.

In some examples, the Lorentz model illustrated by <FIG> may be determined using a minimization algorithm in order to fit the model parameters a, b, c and d to the data in <FIG>. In some examples, the Lorentz model may be determined by using possible angular signatures depending on the energy of the radiation and by calculating the most probable energy of the angular signature using scalar product distance processing.

In some examples, rLf(i) may be determined from rL(i), which is the distance from the source to the pixel (i) (e.g. in mm), as filtered by a moving average with an adjustable radius R, typically between <NUM> and <NUM> pixels, in order to remove strips artifacts. Examples of curves for rLf(i) and rL(i) are illustrated by <FIG>.

From Equations (E10) and (E11), α(i) may thus be determined by: <MAT>.

An example of determination of the Lorentz model is described in <CIT>.

It should be understood that once the three parameters (α(i),µ(i),σ(i)) are determined, for <NUM>≤i≤n, Equation (E1) above may be applied to an inspection image <NUM> generated by the given system <NUM>, such that. <MAT> for <MAT> and <MAT> for <MAT>.

After the transformation has been applied to the inspection image <NUM>, the transformed image is corrupted by an additive Gaussian noise. The method illustrated in <FIG> may further comprise applying, at S3, a denoising filter D to the transformed image f(z(i)). In some examples, D(i) may be a value of the pixel (i) of the denoised image associated with the detector <NUM>(i) of the receiver, after the denoising filter D has been applied to the transformed image, such that: <MAT>.

In some examples, the denoising filter applied to the transformed image comprises at least a Gaussian-based denoising filter. In some examples, the Gaussian-based denoising filter may use Non-Local Means filtering, such as Block Matching <NUM>-Dimensions (BM3D) filtering, and/or Bilateral filtering, and/or Guided filtering, and/or Anisotropic filtering, and/or Gaussian smoothing.

An example of denoising filter is Block Matching <NUM>-Dimensions, which is described in document "<NPL>.

In some examples, in the Block Matching <NUM>-Dimensions (BM3D) filtering, the input noisy transformed image may be processed by successively extracting reference blocks from the transformed image and, for each reference block:.

In some examples, some pixels may get multiple estimates from different groups, because after processing all the reference blocks, the obtained block estimates may overlap. These estimates may be aggregated to form an estimate of the whole image, for example by giving a higher weight to more reliable estimates.

The method illustrated in <FIG> may further comprise applying, at S4, an inverse of the variance-stabilizing transformation to the denoised image.

In some examples, the inverse of the transform may use an unbiased inverse of the Anscombe transformation of S2.

In some examples, a value Id(i) of the pixel (i) of the denoised image associated with the detector <NUM>(i) of the receiver, after the inverse of the variance-stabilizing transformation has been applied, is determined by: <MAT> with D(i) the value of the pixel (i) of the denoised image associated with the detector <NUM>(i) of the receiver, after the denoising filter has been applied to the transformed image.

An example of an unbiased inverse of the Anscombe transformation of S2 is described in the document "<NPL>.

The method illustrated in <FIG> may further comprise, at S5, zooming the inspection image. As explained above, the inspection image has been denoised and the denoised image may be zoomed. The zooming may help the user to detect the hidden objects in the zoomed image, as the zoomed image is also denoised.

As described in further detail below, in some examples zooming may comprise applying bilinear or bicubic interpolations, and/or an upsampling using a deconvolution-based technique. In some examples, zooming may further comprise applying a feedback loop after the upsampling using the deconvolution-based technique has been applied. In some examples, applying the feedback may comprise:.

As shown in <FIG>, in an example, the received inspection image <NUM> discussed above corresponds to a selected zone of interest <NUM> in an image <NUM> generated by the inspection system. In such an example, S2, S3, S4 and S5, as discussed above and described with reference to <FIG>, may be performed for the inspection image <NUM> corresponding to the selected zone <NUM> of interest only, selected in the otherwise noisy image <NUM>. In such an example, processing and computing may thus be reduced, as performed for the image <NUM> only.

As shown in <FIG>, in an example, zooming the inspection image comprises zooming a part <NUM> of the inspection image <NUM>. In the example of <FIG>, the part <NUM> corresponds to the zone of interest <NUM> in the inspection image <NUM>. In such an example, the selection of the part <NUM> may be performed after the whole inspection image <NUM> has been denoised, i.e. after S2, S3 and S4 have been performed on the whole image <NUM>, S5 being performed for the zone <NUM> only. In such an example, the detection of the hidden objects may be enhanced, as the denoising may facilitate the selection of the part <NUM> to be zoomed as a zoomed zone <NUM> as shown in <FIG>.

In some examples, the zone <NUM> of interest may be selected, e.g. based on an input associated with the zone <NUM> of interest. The input associated with the zone <NUM> of interest may be received by the denoiser and/or zoom <NUM> illustrated in <FIG>. In such an example, the input may be provided by the user of the denoiser and/or zoom <NUM>, e.g. using the Graphical User Interface <NUM> (as shown in <FIG>), and/or by the system <NUM>. In some examples, the selection of the zone <NUM> may be performed at least partly automatically by the denoiser and/or zoom <NUM>.

<FIG> shows an example of a zooming of S5 comprising applying an example of a deconvolution-based technique.

The deconvolution-based technique illustrated by <FIG> comprises:.

The example of <FIG> further comprises optionally applying, at S53, a feedback loop to the upsampled zone of interest once the deconvolution-based technique has been applied.

The applying, at <NUM>, of the feedback illustrated in <FIG> comprises:.

The upsampled zone <NUM>' with the pixel substitution may be aggregated with the upsampled zone <NUM>.

An example of deconvolution-based technique of S5 is described in the document "<NPL>.

The radiation source is configured to cause inspection of cargo through the material (usually steel) of walls of the container, e.g. for detection and/or identification of the cargo. Alternatively or additionally, the inspection radiation may be transmitted through the load (the material of the load being thus transparent to the radiation), while the radiation may, at least partly, be reflected by the load (the material and/or composition of the load located being thus only partly transparent to the radiation, and partly reflective to the radiation - in that case, detectors may be placed to receive the radiation reflected by the load).

As explained in greater detail below, in some examples, the inspection system <NUM> may be mobile and may be transported from a location to another location (the system <NUM> may comprise an automotive vehicle). Alternatively or additionally, the inspection system <NUM> may be static with respect to the ground and cannot be displaced.

The electrons are generally accelerated under a voltage comprised between 100keV and 15MeV. The dose of the radiation may be comprised between 2mGy (Gray) and 20Gy.

In mobile inspection systems, the power of the X-ray source <NUM> may be e.g., between 100keV and <NUM>. 0MeV, typically e.g., 300keV, 2MeV, <NUM>. 5MeV, 4MeV, or 6MeV, for a steel penetration capacity e.g., between <NUM> to <NUM>, typically e.g., <NUM> (12in). In static inspection systems, the dose may be e.g., between 20mGy and 120mGy.

In static inspection systems, the power of the X-ray source <NUM> may be e.g., between 4MeV and 10MeV, typically e.g., 9MeV, for a steel penetration capacity e.g., between <NUM> to <NUM>, typically e.g., <NUM> (<NUM>. In static inspection systems, the dose may be 17Gy.

In some examples, the x-ray source <NUM> may emit successive x-ray pulses. The pulses may be emitted at a given frequency, comprised between <NUM> and <NUM>, for example approximately <NUM>.

According to some examples, the detectors <NUM> may be mounted on a gantry <NUM>, as shown in <FIG>. The gantry <NUM> for example forms an inverted "L" extending in a median plane XOY. In mobile inspection systems, the gantry <NUM> may comprise an electro-hydraulic boom which can operate in a retracted position in a transport mode (not illustrated in the Figures) and in an inspection position (<FIG>). The boom may be operated by hydraulic activators (such as hydraulic cylinders).

In static inspection systems, the gantry <NUM> may comprise a static structure.

It should be understood that the inspection radiation source may comprise sources of other radiation, such as, as non-limiting examples, sources of ionizing radiation, for example gamma rays or neutrons. The inspection radiation source may also comprise sources which are not adapted to be activated by a power supply, such as radioactive sources, such as using Co60 or Cs137.

In some examples, the inspection system may comprise other types of detectors, such as optional gamma and/or neutrons detectors, e.g., adapted to detect the presence of radioactive gamma and/or neutrons emitting materials within the load, e.g., simultaneously to the X-ray inspection.

In the context of the present disclosure, the container <NUM> may be any type of container, such as a holder, a vessel, or a box, etc. The container <NUM> may thus be, as non-limiting examples, a trailer and/or a palette (for example a palette of European standard, of US standard or of any other standard) and/or a train wagon and/or a tank and/or a boot of a vehicle such as a truck, a van and/or a car and/or a train, and/or the container <NUM> may be a "shipping container" (such as a tank or an ISO container or a non-ISO container or a Unit Load Device (ULD) container). It is thus appreciated that the container <NUM> may be any type of container, and thus may be a suitcase in some examples.

The system <NUM> is configured to, in the inspection mode, cause inspection of the load, in totality (i.e. the whole load is inspected) or partially (i.e. only a chosen part of the container is inspected, e.g., typically, when inspecting a vehicle, a cabin of the vehicle may not be inspected, whereas a rear part of the vehicle is inspected).

In order to inspect the load, the system <NUM> may comprise a motion generation device so that the system <NUM> may be displaced, the load being static with respect to the ground (this mode is sometimes referred to as a 'scanning' mode). Alternatively or additionally, the motion generation device may cause the load to be displaced, the system <NUM> being static with respect to the ground. In some embodiments, the throughput, i.e. the number of load inspected by unit of time, may be of <NUM> to <NUM> images/hour.

Alternatively or additionally, in a 'pass-through' mode the system <NUM> does not comprise a motion generation device and the load <NUM> moves with respect to the system <NUM>, the system <NUM> being static with respect to the ground. In embodiments, the throughput in the pass-through mode may be higher than the throughput in the scanning mode, and may be for example of <NUM> to <NUM> images/hour, or even of <NUM> to a few thousands images/hour in the case of an inspection of a passing train (for example a throughput of more than <NUM> images/hour).

Other variations and modifications of the system or the analyser will be apparent to the skilled in the art in the context of the present disclosure, and various features described above may have advantages with or without other features described above.

For example, the denoiser and/or zoom <NUM> may, at least partly, form a part of the inspection system <NUM>. For example, the server <NUM> may, at least partly, form a part of the denoiser and/or zoom <NUM>.

As one possibility, there is provided a computer program, computer program product, or computer readable medium, comprising computer program instructions to cause a programmable computer to carry out any one or more of the methods described herein. In example implementations, at least some portions of the activities related to the denoiser and/or zoom <NUM> and/or the links <NUM> and/or <NUM> herein may be implemented in software. It is appreciated that software components of the present disclosure may, if desired, be implemented in ROM (read only memory) form. The software components may, generally, be implemented in hardware, if desired, using conventional techniques. In some examples, components of the denoiser and/or zoom <NUM> and/or the links <NUM> and/or <NUM> may use specialized applications and hardware.

As will be apparent to the skilled in the art, the server <NUM> should not be understood as a single entity, but rather refers to a physical and/or virtual device comprising at least a processor and a memory, the memory may be comprised in one or more servers which can be located in a single location or can be remote from each other to form a distributed network (such as "server farms", e.g., using wired or wireless technology).

In some examples, one or more memory elements (e.g., the database and/or the memory of the processor) can store data used for the operations described herein. This includes the memory element being able to store software, logic, code, or processor instructions that are executed to carry out the activities described in the disclosure.

A processor can execute any type of instructions associated with the data to achieve the operations detailed herein in the disclosure. In one example, the processor could transform an element or an article (e.g., data) from one state or thing to another state or thing. In another example, the activities outlined herein may be implemented with fixed logic or programmable logic (e.g., software/computer instructions executed by a processor) and the elements identified herein could be some type of a programmable processor, programmable digital logic (e.g., a field programmable gate array (FPGA), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM)), an ASIC that includes digital logic, software, code, electronic instructions, flash memory, optical disks, CD-ROMs, DVD ROMs, magnetic or optical cards, other types of machine-readable mediums suitable for storing electronic instructions, or any suitable combination thereof.

The links <NUM> and/or <NUM> may form only one network.

The data received by the denoiser and/or zoom <NUM> may be typically received over a range of possible communications networks, at least such as: a satellite based communications network; a cable based communications network; a telephony based communications network; a mobile-telephony based communications network; an Internet Protocol (IP) communications network; and/or a computer based communications network.

In some examples, the denoiser and/or zoom <NUM> and/or the links <NUM> and/or <NUM> may comprise one or more networks. Networks may be provisioned 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), Intranet, Extranet, any other appropriate architecture or system, or any combination thereof that facilitates communications in a network.

Claim 1:
A computer-implemented method of denoising one or more inspection images (<NUM>) comprising a plurality of pixels (<NUM>), comprising:
receiving (S1) an inspection image (<NUM>) generated by an inspection system (<NUM>) configured to inspect one or more containers (<NUM>), wherein
the inspection system (<NUM>) is configured to inspect the container (<NUM>) by transmission, through the container, of inspection radiation (<NUM>, <NUM>), the inspection radiation (<NUM>, <NUM>) having an angular divergence divergent from a focal point of an inspection radiation source (<NUM>) to an inspection radiation receiver (<NUM>, <NUM>) comprising a plurality of detectors (<NUM>), the detectors (<NUM>) being adjacent to one another to form a straight line array (<NUM>) of detectors and being associated with the pixels (<NUM>) in the inspection image (<NUM>), each detector (<NUM>) being capable of receiving an individual angular sector of the inspection radiation (<NUM>, <NUM>), and
the inspection image (<NUM>) is corrupted by a Poisson-Gaussian noise and a variance of the noise being non-constant in the plurality of pixels (<NUM>); and
denoising (S2) the received inspection image (<NUM>) by applying, to the inspection image (<NUM>), a variance-stabilizing transformation for transforming the variance of the noise into a constant variance in the plurality of pixels (<NUM>), wherein the variance-stabilizing transformation is based on a descriptor (<NUM>) associated with the angular divergence of the individual angular sector of
the inspection radiation (<NUM>, <NUM>) and the Poisson-Gaussian noise, the descriptor being associated with each pixel (<NUM>) of the plurality of pixels of the inspection image (<NUM>).