Method for compensating for drifts experienced by the pixels of a terahertz image sensor

Abstract

The invention relates to a method for compensating for pixel drifts of a matrix image sensor, comprising the steps of recording a reference dark image corresponding to an image acquired by the sensor in the absence of illumination; acquiring a current image (P ij) by the sensor by masking pixels at the periphery of the sensor matrix; recording a pixel (P 12,0) of the current image located in the masked part of the matrix as a compensation pixel; and compensating a group of several pixels of the current image using the dark image and the compensation pixel. Figure for abstract: Fig. 3A

Description

Title of the invention: Method for compensating for drifts experienced by the pixels of a terahertz image sensor Technical field

[0001] The invention relates to real-time quality control on a production line using terahertz image sensors, and more specifically to the compensation of disparities between the pixels of the sensors. Background

[0002] Mass production of certain products on conveyor belts involves precise and regular dosing of a given material, such as a super-absorbent polymer commonly referred to as SAP in the manufacture of diapers.

[0003] As described in patent application WO2018204724, terahertz imaging proves to be particularly well suited to the control of SAP dosage on a conveyor belt. A frequency around 300 GHz makes it possible to acquire density images of SAP clusters deposited on the belt. For this, typically, a row of several terahertz cameras is arranged transversely to the belt and a row of terahertz sources is arranged to illuminate the cameras through the belt and the conveyed products.

[0004] The operation of such an installation suffers from difficulties linked to the disparities between the pixels of the sensors and to the signal-to-noise ratio. Summary

[0005] A method for compensating for pixel drifts of a matrix image sensor is generally provided, comprising steps consisting of recording a reference dark image corresponding to an image acquired by the sensor in the absence of illumination; acquiring a current image by the sensor by masking pixels at the periphery of the sensor matrix; recording a pixel of the current image located in the masked part of the matrix as a compensation pixel; and compensating a group of several pixels of the current image by using the dark image and the compensation pixel.

[0006] The method may further comprise steps of recording a compensation pixel located in each of several rows of pixels of the current image; and compensating the pixels of each of the rows using the respective compensation pixel.

[0007] The method may further comprise steps of acquiring the current image by reading the pixels of the sensor in rows; recording a compensation pixel located in each of several columns of pixels of the current image; and compensating the pixels of each of the columns using the compensation pixel. respective.

[0008] The compensating step may comprise subtracting the corresponding pixels from the dark image and subtracting a deviation provided by the compensating pixel.

[0009] The method may comprise steps consisting of observing a conveyor belt of regularly spaced products with the image sensor; illuminating the image sensor through the belt with a radiation source, the belt being transparent to the radiation; recording with the image sensor the intensities of a strip of pixels transverse to the direction of travel of the belt between two consecutive products; acquiring an image of a current product; and in the acquired image, calculating absorptions using the intensities recorded for the belt.

[0010] The radiation may be in the terahertz range and the products may comprise a super-absorbent polymer.

[0011] It is also possible to provide in general a method for compensating for lighting drifts of a production line observed by a matrix image sensor, comprising steps consisting of observing a conveyor belt of regularly spaced products with the image sensor; illuminating the image sensor through the belt with a radiation source, the belt being transparent to the radiation; recording with the image sensor the intensities of a strip of pixels transverse to the direction of travel of the belt between two consecutive products; acquiring an image of a current product; and in the acquired image, calculating absorptions using the intensities recorded for the strip.

[0012] The radiation may be in the terahertz range and the objects may comprise a super-absorbent polymer. Summary description of the drawings

[0013] Embodiments will be set out in the following description, given without limitation in relation to the attached figures among which:

[0014] [Fig.1A] shows examples of raw images captured by three aligned cameras observing respective sources of terahertz radiation;

[0015] [Fig.lB] represents examples of images obtained by the same cameras in the absence of a source;

[0016] [Fig.lC] represents images obtained by subtracting the images of [Fig.lB] from the images of [Fig.lA];

[0017] [Fig.2] illustrates an example of a pixel matrix of a terahertz image sensor, exposed only in the central part, according to one embodiment;

[0018] [Fig.3A], [Fig.3B] and [Fig.3C] illustrate different modes of using pixels of the matrix of [Fig.2] to compensate for drift;

[0019] [Fig.4A], [Fig.4B], [Fig.4C] and [Fig.4D] represent variations over time of the weight measurements obtained by the same imager on the same sample for different compensation modes; and

[0020] [Fig.5A] and [Fig.5B] represent an image obtained from a section of conveyor belt transporting layers and the detail of a layer. Detailed description

[0021] Terahertz image sensors, given the low level of the processed signals, are sensitive to drifts experienced both during manufacture and during use, particularly thermal drifts. Added to this is the low power of the terahertz sources and the resulting inhomogeneity of the illumination of the pixel matrices of the sensors.

[0022] [Fig.lA] shows examples of raw images captured by three aligned cameras directly observing respective sources of terahertz radiation. Each camera has a matrix of 13x13 pixels, for example, and the intensities measured by the pixels are represented in a gray scale ranging from white for 0 to black for the maximum measured intensity.

[0023] Ideally, images should be black, with each pixel receiving the same maximum intensity. However, images are marred by noise and the lack of uniformity of terahertz sources.

[0024] [Fig.lB] shows examples of images obtained by the same cameras when the sources are switched off, namely dark images. Ideally, these images should be white. However, the pixels provide random non-zero values ​​in a certain range of intensities. Such images generally depend on the disparities encountered during the manufacture of the sensors and they remain substantially constant over time, at least as regards the relative levels between the pixels. The absolute levels tend to vary as a function of temperature.

[0025] Such a reference dark image may be recorded for each camera at the start of a production phase. The dark image is then subtracted from each current acquired image to produce a noise-compensated image.

[0026] [Fig.lC] represents compensated images obtained by subtracting the dark images of [Fig.lB] from the raw images of [Fig.lA]. These images reveal that the terahertz radiation sources only illuminate a central area of ​​the pixel matrix, and this with an intensity which decreases from the center towards the edges, according to a profile which varies from one camera to another.

[0027] The intensity profiles thus obtained serve as a reference for mapping the radiation absorption of an object placed between the sources and the cameras. The absorption for each pixel corresponds to the ratio between the measured signal and the corresponding reference intensity of the reference profile.

[0028] Despite the fact that these measures compensate for the lack of uniformity of the sources and the dark images, there remain drifts over time of the dark images, sources and other elements. Pixel readout circuits also tend to drift with temperature, as do the gain and offset of the amplifiers used to extract pixel measurements.

[0029] It is difficult in practice to record dark images and intensity profiles of the sources regularly enough to take into account drift over time. Indeed, this involves, for the dark images, regularly turning off the sources or masking the camera to capture several dark images which are averaged to reduce noise. Each of these phases occupies a time interval during which the production line is stopped. Even if the time required to produce a reference dark image is relatively short, stopping and restarting production takes a significant amount of time. If production is not stopped, a certain number of products would pass without being checked, which is not acceptable in certain installations.

[0030] A method is proposed below for taking into account drifts of a dark image and other elements of a terahertz image sensor that does not require stopping the production line or abandoning product control.

[0031] [Fig.2] illustrates an image acquisition configuration for implementing the process. The pixel array of the image sensor is exposed only in the central part, leaving the pixels at the periphery in the dark. The optical system of the sensor is thus configured to focus the scene to be observed, for example a part of the conveyor belt, in a circle inscribed in the pixel array. Ideally, the circle can be made to correspond with the area of ​​concentration of the illumination of the corresponding terahertz source ([Fig.lA]).

[0032] The pixels outside the circle can thus be masked by the optical system or by metallizations deposited on the pixels during the manufacture of the sensor, or any other masking means.

[0033] [Fig.3A] illustrates a first mode of use of the pixels of an image acquired under the conditions of [Fig.2]. The complete matrix has, for example, a dimension of 13x13 pixels. The rows and columns of the matrix are identified by numbers from 0 to 12 starting from the upper left corner.

[0034] In this matrix, we use a square area of ​​7x7 pixels, circumscribed in the circle, represented by pixels in bold lines.

[0035] The pixels of the masked part represent partially but in real time the dark image to be subtracted from the raw image. According to the simplified hypothesis that a thermal drift affects all the pixels homogeneously, the complete dark image can be reconstructed by using the drift measurable in real time of any one of the pixels of the masked part of the matrix, for example the pixel at the lower left corner Pn,o, where Pÿ denotes the pixel of row i and column j of the image current acquired

[0036] More specifically, if DOy are the pixels of the dark image recorded at a time t0 at the start of a production phase, each pixel of the current dark image becomes:

[0037] Dy=f(P12,o,DOy).

[0038] Where / is a function depending on the model used to characterize the drift. According to an assumption where the drift results in a constant shift applicable to all pixels in the matrix, we have:

[0039] Dy = (P12fi - D0I20) + DOy.

[0040] According to the hypothesis where the drift is a proportional scale drift, we have:

[0041] Dy = (P]2 0 / D0]2 0) * DOy.

[0042] Both hypotheses provide close results when the drift is small and the disparities in the dark image are small. Reality may be a combination of both types of drift.

[0043] Each pixel of the compensated image is expressed by:

[0044] Cy = P y- Dy.

[0045] In a flow involving fewer calculations, we can start by subtracting the dark image D0 from the current image P to form an intermediate image I which would be the compensated image in a process without drifts:

[0046] ly = P y- DOy.

[0047] In the final compensation, we use the pixel II2fi of the intermediate image that we have just calculated instead of the pixel PI2fi. In the hypothesis of a drift by offset, we obtain:

[0048] Cy = ly - I12.0.

[0049] A pixel matrix generally has circuits in common per row and other circuits in common per column, such as a selection circuit per row, and a reading circuit per column, typically including an amplifier. These circuits are also likely to cause drifts.

[0050] [Fig.3B] illustrates a second mode of use of the pixels of an image acquired under the conditions of [Fig.2], designed to take into account different drifts per row of pixels.

[0051] For each row 3 to 9 of the useful area of ​​the pixel matrix, a masked pixel of the same row is used for compensation, for example pixels P3fi to P9:0 in the first column. Each of these compensation pixels is used to compensate only the pixels of the same row. We thus obtain, assuming a drift by offset:

[0052] Cy = ly - Ij#, with j varying from 3 to 9.

[0053] [Fig.3C] illustrates a second mode of using the pixels of an acquired image under the conditions of [Fig.2], designed to take into account different drifts per pixel column. As shown, each column is generally connected to an amplifier used to adapt the impedance of the pixel outputs and to amplify the pixel signal, if necessary. An amplifier is susceptible to drifts in gain and offset depending on the temperature.

[0054] Here, for each column 3 to 9 of the useful area of ​​the pixel matrix, a masked pixel from the same column is used for compensation, for example pixels P]2,3 to P 12,9 in the last row. Each of these compensation pixels is used to compensate only the pixels of the same column. We thus obtain, assuming a drift by offset:

[0055] Cy = ly - I]2j, with j varying from 3 to 9.

[0056] The resulting compensated images provide a map of the absorption rates of the observed material, for example SAP. The absorption rate provided by each pixel is converted into a specific mass according to a Beer-Lambert chart for the frequency and the material. The sum (or integration) of the specific masses over the surface of the image of the observed product provides the mass of the material in the product, or grammage.

[0057] [Fig.4A] to 4D represent variations over time of the grammage measurements obtained by the same imager on the same sample for the different compensation modes described above, assuming a drift by offset. A light horizontal line represents the actual grammage, here 23.28 g. The black dots represent the different grammage measurements and the gray dots represent the average of the measurements per time slice. The divisions of the time axis correspond to 2 h. The measurements were made over a period of 16 h.

[0058] [Fig.4A] represents the measurements made with simple compensation by a dark image. Variations between 21.5 and 24.3 g are observed. The standard deviation is 0.35. This is the worst result.

[0059] [Fig.4B] represents the measurements made with column compensation ([Fig.3C]). Variations between 23.2 and 23.6 g are observed. The standard deviation is 0.05. This is the best result.

[0060] [Fig.4C] represents the measurements made with row compensation ([Fig.3B]). Variations between 22.8 and 23.6 g are observed. The standard deviation is 0.08.

[0061] [Fig.4D] represents the measurements made with compensation by a single masked pixel ([Fig.3A]). Variations between 22.8 and 23.8 g are observed. The standard deviation is 0.11.

[0062] The column compensation of [Fig.4B] is clearly the most effective, which suggests that the circuits associated with the columns, in particular the amplifiers, exert a significant influence on the drifts, and that they drift in a non- homogeneous according to the constant shift hypothesis.

[0063] According to a variant, it can be assumed that the drift undergone by a pixel is correlated to the amplitude of its dark signal. It is then possible to consider applying a compensation proportional to the amplitude of the dark signal instead of a constant offset, or a combination of the two compensations, with a constant component and a proportional component.

[0064] The row-based and single-masked pixel compensations of Figures 4C and 4D, although significantly less effective than the method of [Fig.4D], remain significantly more effective than simple dark image compensation.

[0065] Many variations of the compensation methods described are possible. For example, row-wise and column-wise compensation can be combined. Instead of using a single masked pixel to determine a compensation to be applied, the compensation can be calculated as the average of several masked pixels, for example several pixels distributed around the matrix or two pixels on either side of each column or row. Of several candidate masked pixels, the one that provides the highest value can be used...

[0066] In the above, a method for compensating for drifts over time experienced by the darkness signals of the pixels has been described. In the context of an inspection carried out on a conveyor belt illuminated over long periods by the same terahertz radiation sources, these sources may also undergo intensity drifts over time, modifying their intensity profiles. A method for compensating for such drifts is described below.

[0067] [Fig.5A] represents an example of an image obtained from a section of conveyor belt transporting layers. The image is produced by a row comprising, for example, 9 cameras of 13x13 pixels of which the central 7x7 pixels are used, in accordance with the examples of the previous figures. The image of [Fig.5A] comprises four rows of layers which correspond to consecutive sections of the belt. The scales of the axes are in millimeters. The gray levels correspond to the intensity transmitted to the cameras, white being the maximum intensity and black the minimum intensity.

[0068] Furthermore, a compensation for drift of the dark images was made according to the column compensation described above.

[0069] [Fig.5B] is an enlarged view of one of the layers. It can be seen that the image has horizontal streaking caused in particular by the lack of homogeneity of the lighting received by each camera. This streaking is normally compensated for by calculating the absorptions based on the reference intensity profiles of [Fig.1C] and does not present any particular difficulties.

[0070] The reference intensity profiles of terahertz sources are, on the other hand, sus- likely to drift over time, for example depending on the temperature or humidity of the ambient air. For illustration, the first and third rows of [Fig.5A] have darker parts, representing a downward drift in the intensities of the sources.

[0071] A conveyor belt used in the manufacture of layers is generally perfectly transparent to terahertz radiation around 300 GHz, without which terahertz radiation control would be less effective. Thus, in the areas around the layers, the effective intensity of the sources can be acquired with satisfactory accuracy.

[0072] According to an embodiment for compensating for drifts, provision is made in a first step to record a reference pixel strip transverse to the direction of travel of the belt between each pair of consecutive layers. These reference strips are drawn in black in [Fig.5A] and a corresponding line is designated by 50 in [Fig.5B].

[0073] In practice, the useful areas of the cameras are used in global shutter mode to capture successive rectangles of the belt as it moves. These rectangles are 7x7 pixel squares according to the previous examples. The cameras can be aligned so that the captured rectangles are transversely contiguous and synchronized with the movement of the belt so that the rectangles captured by each camera are longitudinally contiguous. This creates a complete image of the belt. In one example, each pixel covers a 4x4 mm square of the belt, so the useful area of ​​each camera covers a 28x28 mm square of the belt. The multiple cameras then capture 28 mm wide transverse slices and are synchronized to capture a new slice for each 28 mm movement of the belt.

[0074] When a gap between two layers is identified, for example when the intensity exceeds a threshold for all camera pixels, the intensities of the pixels corresponding to the reference band among the newly acquired pixels are recorded as a new intensity profile to be used for calculating absorptions up to the next band. Reference bands may typically correspond to slices as captured by the cameras. A band may also straddle slices, or be narrower than a slice.

Claims

Claims

1. A method for compensating for pixel drifts of a matrix image sensor, comprising the following steps: recording a reference dark image corresponding to an image acquired by the sensor in the absence of illumination; acquiring a current image (Py) by the sensor by masking pixels at the periphery of the matrix of the sensor; recording a pixel (Pn,o) of the current image located in the masked portion of the matrix as a compensation pixel; and compensating a group of several pixels of the current image by using the dark image and the compensation pixel.

2. A method according to claim 1, comprising the steps of: recording a compensation pixel (P3,o—P9,0) located in each of several rows of pixels of the current image; and compensating the pixels of each of the rows using the respective compensation pixel.

3. A method according to claim 1, comprising the steps of: acquiring the current image by reading the pixels of the sensor in rows; recording a compensation pixel (Pi2,3—Pn,9) located in each of several columns of pixels of the current image; and compensating the pixels of each of the columns using the respective compensation pixel.

4. The method of claim 1, wherein the compensating step comprises subtracting the corresponding pixels from the dark image and subtracting a deviation provided by the compensating pixel.

5. A method according to claim 1, comprising the following steps: observing a conveyor belt of regularly spaced products with the image sensor; illuminating the image sensor through the belt with a radiation source, the belt being transparent to the radiation; recording with the image sensor the intensities of a strip of pixels (50) transverse to the direction of travel of the belt between two consecutive products; acquiring an image of a current product; and in the acquired image, calculating absorptions using the intensities recorded for the strip.

6. The method of claim 5, wherein the radiation is in the terahertz range and the products comprise a superabsorbent polymer.

7. Method for compensating for lighting drifts of a production line observed by a matrix image sensor, comprising the following steps: observing a conveyor belt of regularly spaced products with the image sensor; illuminating the image sensor through the belt with a radiation source, the belt being transparent to the radiation; recording with the image sensor the intensities of a strip of pixels (50) transverse to the direction of travel of the belt between two consecutive products; acquiring an image of a current product; and in the acquired image, calculating absorptions using the intensities recorded for the strip.

8. The method of claim 7, wherein the radiation is in the terahertz range and the objects comprise a superabsorbent polymer.

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