Method for performing spatial mode-selective imaging, corresponding system and computer program product

Abstract

A method for performing spatial mode-selective imaging of a sample (Obj), such method comprising: projecting (104) a random coherent light pattern on the sample (Obj), such random coherent light pattern comprising a coherent light wave (106) and a random noise pattern (102a); obtaining a first image signal and a second image signal (112) as a function of a transmittance of the sample (Obj) and of such random coherent light pattern, such first image signal being in a spatial domain and such second image signal (112) being in a frequency domain; computing a correlation between such first image signal and such second image signal; and filtering such correlation in the frequency domain by applying a given frequency filtering function thereto, preferably wherein such operation of applying the given frequency filtering function to the correlation is performed by multiplying such correlation for such given frequency filtering function.

Description

[0001] "Method for performing spatial mode-selective imaging , corresponding system and computer program product"

[0002] ★ ★ ★ ★

[0003] TEXT OF DESCRIPTION

[0004] Technical field

[0005] The description relates to methods for performing spatial mode-selective imaging .

[0006] One or more embodiments can be related to methods for performing spatial frequency-selective imaging, that is , spatial mode-selective imaging, of non-phase obj ects and / or phase obj ects .

[0007] Background

[0008] Spatial frequency-selective imaging ( also referred to as spatial mode-selective imaging) techniques are methods that use light to extract information, represented via one or more images , related to features of obj ects by analyzing certain spatial frequency components ( also referred to as spatial modes ) .

[0009] Spatial frequency-selective imaging techniques are widely used in various applications such as , for instance , industrial inspection and quality control applications since they allow the detection and analysis of defects , inconsistencies , and other critical features of manufactured products and materials .

[0010] In fact , by isolating speci fic frequency components , spatial frequency-selective imaging can provide detailed insights that cannot be achieved with traditional imaging methods .

[0011] A problem related to known spatial frequency- selective imaging techniques is that they do not allow filtering the spatial frequency content comprised in an image of a sample that is to be analysed .

[0012] In fact , conventional imaging devices collect images of samples resulting from the superposition of the whole frequency content of the sample , while the modal filtering is typically obtained by introducing hardware frequency filters inside of the optical setup .

[0013] Spatial filtering, DC removal , and phase imaging are typically achieved, in conventional imaging devices , by operating directly on the device by inserting filters or adapting the illumination strategy to gain sensitivity to the features of the sample to which one is interested .

[0014] For instance , spatial filtering can be obtained, in conventional microscopes composed by an obj ective and a tube lens , by gaining access to the second focal plane of the obj ective and inserting spatial filters whose shape is tailored to the speci fic modal content to which sensitivity is needed . In many cases , the same strategy can be used to gain sensitivity to the phase content of the sample .

[0015] Other conventional strategies , for instance , the dark- field imaging, are usually implemented by operating on the illumination stage in order to make the imaging system sensitive only to the light scattered or di f fused by the sample , and not directly to the illumination light . I llumination at high angles can also be used to gain sensitivity to some phase features of the samples .

[0016] Therefore , solutions allowing spatial frequency filtering, for instance , in post-processing, would be beneficial .

[0017] Obj ect and summary

[0018] An obj ect of one or more embodiments is to contribute in providing solutions allowing filtering the spatial frequency content comprised in an image of a sample that is to be analyzed .

[0019] According to one or more embodiments , that obj ect is achieved via a method for performing spatial mode- selective imaging having the features set forth in the claims that follow .

[0020] Solutions as described herein are related to a method for performing spatial mode-selective imaging of a sample , said method comprising : proj ecting a random coherent light pattern on the sample , said random coherent light pattern comprising a coherent light wave and a random noise pattern; obtaining a first image signal and a second image signal as a function of a transmittance of the sample and of said random coherent light pattern, said first image signal being in a spatial domain and said second image signal being in a frequency domain; computing a correlation between said first image signal and said second image signal ; and filtering said correlation in the frequency domain by applying a given frequency filtering function thereto , preferably wherein said operation of applying the given frequency filtering function to the correlation is performed by multiplying said correlation for said given frequency filtering function .

[0021] In various embodiments , solutions as described herein are related to a method further comprising obtaining a filtered transmittance of the sample by integrating in the frequency domain the computed correlation having applied thereto said given frequency filtering function .

[0022] One or more embodiments concern a corresponding system .

[0023] One or more embodiments concern a corresponding computer program product loadable in at least one processing circuit ( e . g . , a computer ) and comprising software code portions for executing the steps of the method when the product is run on at least one processing circuit . As used herein, reference to such a computer program product is understood as being equivalent to reference to a computer-readable medium containing instructions for controlling a processing system in order to coordinate implementation of the ( corresponding) method according to one or more embodiments .

[0024] The claims are an integral part of the technical teaching provided in respect of the embodiments .

[0025] Therefore , solutions as described herein facilitate achieving a filtering of the spatial frequency content comprised in an image of a sample that is to be analyzed .

[0026] Brief description of the figures

[0027] One or more embodiments will now be described, by way of example only, with reference to the annexed figures , wherein :

[0028] Figures 1A and IB illustrate exemplary systems configured to acquire spatial and / or modal information about a sample that is to be analyzed according to embodiments of the present description;

[0029] Figures 2 and 3 illustrate exemplary reconstructions of an exemplary non-phase double-slit sample obtained using the system of Figure 1A according to embodiments of the present description;

[0030] Figure 4 illustrates a two-dimensional imaging of simulated cells assumed as non-phase obj ect ;

[0031] Figure 5 illustrates exemplary phase-contrast microscopy of a pure-phase obj ect according to embodiments of the present description; and

[0032] Figure 6 illustrates another exemplary system configured to acquire spatial and / or modal information about a sample that is to be analyzed according to embodiments of the present description .

[0033] Corresponding numerals and symbols in the di f ferent figures generally refer to corresponding parts unless otherwise indicated . The figures are drawn to clearly illustrate the relevant aspects of the embodiments and are not necessarily drawn to scale .

[0034] The edges of features drawn in the figures do not necessarily indicate the termination of the extent of the feature .

[0035] Detailed description

[0036] In the ensuing description one or more speci fic details are illustrated, aimed at providing an in-depth understanding of examples of embodiments of this description . The embodiments may be obtained without one or more of the speci fic details , or with other methods , components , materials , etc . In other cases , known structures , materials , or operations are not illustrated or described in detail so that certain aspects of embodiments will not be obscured .

[0037] Reference to "an embodiment" or "one embodiment" in the framework of the present description is intended to indicate that a particular configuration, structure , or characteristic described in relation to the embodiment is comprised in at least one embodiment . Hence , phrases such as " in an embodiment" or " in one embodiment" that may be present in one or more points of the present description do not necessarily refer to one and the same embodiment .

[0038] Moreover, particular conf igurations , structures , or characteristics may be combined in any adequate way in one or more embodiments .

[0039] The headings / ref erences used herein are provided merely for convenience and hence do not define the extent of protection or the scope of the embodiments .

[0040] For simplicity and ease of explanation, throughout this description, and unless the context indicates otherwise , like parts or elements are indicated in the various figures with like reference signs , and a corresponding description will not be repeated for each and every figure .

[0041] As previously described, solutions as described herein aim at providing a filtering of the spatial frequency content comprised in an image of a sample that is to be analyzed .

[0042] Therefore , solutions as described herein aim at isolating, for any spatial frequency comprised within a sample of interest that is to be analyzed, the contribution of that spatial frequency to the image of the sample .

[0043] Solutions as described herein are related to a method for performing spatial frequency-selective imaging, that is , spatial mode-selective imaging, using a position-momentum correlation of a sample that is to be analyzed .

[0044] In particular, solutions as described herein may allow, with a single measurement , to perform such filtering of the spatial frequency at a same time and / or for the whole two-dimensional spectrum of spatial frequencies based on such position-momentum correlation, said position-momentum correlation being evaluated between coherent patterns produced through fixed-phase intensity modulation .

[0045] In addition, solutions as described herein allow to perform such spatial frequency filtering in postprocessing, for instance , via a (possibly distributed) processing unit such as a microcontroller, a microprocessor, a logic unit , or the like , without adding any hardware component to the imaging device for that purpose .

[0046] Such a post-processing filtering is advantageous since it allows to add versatility in the spatial frequency filtering operation without modi fying the imaging device . In fact , solutions as described herein are able to collect more information related to the sample i f compared to the two-dimensional image resulting from the superposition of the whole frequency content of the sample obtained via conventional imaging systems , thus , allowing the user to perform a wider variety of operations on the sample without needing manipulation of the imaging device setup .

[0047] In addition, solutions as described herein are configured to measure a signed real quantity instead of a positive intensity distribution as conventional imaging techniques do , allowing the extraction of more information from a single measure of the sample .

[0048] It is noted that the spatial frequency-selective imaging described herein, allowing a post-processing filtering of the spatial frequencies , can improve the resolution of the resulting image exploiting the more information related to the sample collected and without using sophisticated optics and optomechanics .

[0049] Figures 1A and IB illustrate exemplary systems 10aand 10b configured to acquire , at a same time , spatial and / or modal information about a sample , for instance , an obj ect Ob , that is to be analyzed according to embodiments of the present description .

[0050] Such operation o f acquiring the spatial and modal information is performed by considering correlations between coherent patterns , that is , patterns having a two-dimensional intensity distribution characteri zed by a flat ( that is , uni form) phase profile and generated by a coherent light source ( that is , a light source featuring wavefronts that are spatially coherent in a mutual fashion, e . g . a laser light ) .

[0051] Figure 1A illustrates a first exemplary system 10aconfigured to acquire , at a same time , spatial and modal information about a sample , for instance , an obj ect Ob j , that is to be analyzed .

[0052] Such first system 10amay comprise a first subsystem configured to proj ect a random coherent intensity pattern on the sample Ob , such first subsystem comprising : a light source 106 , for instance , a laser source , configured to send a generated light wave having a constant phase profile , for instance a plane wave, on an intensity modulator 104 ; a noise pattern generator 102aconfigured to generate a noise pattern, for instance , a noise pattern known a pri ori or an unknown noise pattern, and to drive the intensity modulator 104 via such generated noise pattern; and the intensity modulator 104 configured to receive the generated light wave from the light source 106 and the generated noise pattern from the noise pattern generator 102a, to obtain the random coherent intensity pattern based on such generated light wave and such generated noise pattern, and to proj ect such random coherent intensity pattern on the sample Obj , for instance , via a relay lens 108 , that is , a lens or a group of lenses receiving an image and relaying it to a di f ferent focus plane , or, more in general , a relay optical assembly .

[0053] In such a way, by relaying the random coherent intensity pattern from the intensity modulator 104 to a plane of the sample Obj , the transmission function of the sample Obj is modulated with a coherent mask af fected by the random noise , that is , a coherent mask comprising a random distribution of spatial modes .

[0054] The first system 10amay comprise a second subsystem configured to observe , at a same time and for each proj ected random coherent intensity pattern, a far- field 110 image and a corresponding near- field image . To such purpose , the second subsystem may comprise a modal detector 112 configured to detect such far- field image and a spatial detector 120 configured to detect such corresponding near- field image , for instance , where such modal detector 112 is synchroni zed with such spatial detector 120 in order to collect corresponding images .

[0055] For instance , the far- field image obtained by the modal detector 112 can be obtained via a first lens 114 used to observe the signal transmitted by the sample Obj in its Fourier plane , that is , in a second focal plane of such first lens 114 , or, more in general , via any optical assembly configured to perform a Fourier trans formation of the signal transmitted by the sample Obj .

[0056] Therefore , references in the following description to the first lens 114 may also be intended as references to an optical assembly configured to perform a Fourier trans formation of the signal transmitted by the sample Obj .

[0057] For instance , the corresponding near- field image obtained by the spatial detector 120 ( at the same time of the collection of the far- field image ) can be obtained, in general , via any optical assembly configured to detect the signal transmitted by the sample obj ect Obj .

[0058] Preferably, such spatial detector 120 may be obtained via a 4- f optical system, that is , a system comprising such first lens 114 configured to perform a Fourier trans formation on the signal transmitted by the sample Obj ( or the optical assembly configured to perform such Fourier trans formation) and a second lens 118 configured to restore the received signal in order to obtain the near- field image ( or, more in general , any optical assembly configured to restore the signal transmitted by the sample obj ect Obj from its Fourier trans formation) .

[0059] Therefore , references in the following description to the spatial detector 120 , the 4- f optical system, or to the second lens 118 may also be intended as references to an optical assembly configured to detect the signal transmitted by the sample obj ect Ob .

[0060] Such second subsystem may comprise a beam splitter 116 configured to receive the Fourier trans formation of the signal transmitted by the sample Obj output by the first lens 114 and to provide such Fourier trans formation to the modal detector 112 and the second lens 118 .

[0061] Referring to the generali zation described above, that is , to the fact that the far- filed image may be obtained via any optical assembly configured to perform a Fourier trans formation of the signal transmitted by the sample Obj and that the corresponding near- filed image may be obtained via any optical assembly configured to detect the signal transmitted by the sample obj ect Obj , Figure 6 il lustrates another exemplary system 10cconfigured to acquire spatial and / or modal information about a sample Obj that is to be analyzed according to embodiments of the present description .

[0062] It is noted that Figure 6 illustrates the noise pattern generator 102a, the intensity modulator 104 , the light source 106 , and the relay lens 108 , that is , the first subsystem, already described for Figure 1A, therefore , a description of such elements and o f the first subsystem will not be repeated in the following in order to not overburden the present description .

[0063] The exemplary system 10cmay comprise a second subsystem configured to observe , at a same time and for each proj ected random coherent intensity pattern, a far- field image and a corresponding near- field image .

[0064] To such purpose , the second subsystem of the exemplary system 10cmay comprise the modal detector 112 configured to detect such far- field image and the spatial detector 120 configured to detect such corresponding near- field image .

[0065] Again, the modal detector 112 is synchroni zed with the spatial detector 120 in order to collect corresponding images .

[0066] In solutions according to Figure 6 , the near- field image obtained by the spatial detector 120 can be obtained via a f irst optical assembly 122 and the far- field image obtained by the modal detector 112 can be obtained via a second optical assembly 124 .

[0067] Such first optical assembly 122 , for instance , a lens or a more complex optical system, is configured to detect the signal transmitted by the sample obj ect Ob and to provide an observation of the signal transmitted by the sample obj ect Obj to the spatial detector 120 .

[0068] The second optical assembly 124 , for instance , a lens or a more complex optical system, is configured to perform ( at the same time of the collection of the near- field image ) a Fourier trans formation of the s ignal transmitted by the sample Obj , therefore , such second optical assembly 124 can be configured to observe the signal transmitted by the sample Obj in its Fourier plane and to provide the observation of the signal in the Fourier plane to the modal detector 112 .

[0069] It is noted that the first optical assembly 122 can be completely independent from the second optical assembly 124 , therefore , the two optical assembly may be designed independently .

[0070] It is noted that the signal transmitted by the sample obj ect Obj may be received by a beam splitter 116 , such beam splitter 116 being configured to receive such signal transmitted by the sample obj ect Obj and to provide it to the first optical assembly 122 and the second optical assembly 124 , thus , such signal transmitted by the sample obj ect Ob can be divided in di f ferent optical paths and sent to respective optical assembly to obtain the modal and spatial information .

[0071] Figure IB illustrates a second exemplary system 10b configured to acquire modal information about a sample , for instance , an obj ect Obj , that is to be analyzed .

[0072] Such second system 10 may comprise a first subsystem, such first subsystem being equal to that already described for the first system 10a, therefore , the description of such previously described first subsystem will not be repeated in the following in order to not overburden the present description .

[0073] The second system 10b may comprise a second subsystem configured to observe , for each proj ected random coherent intensity pattern, a far- field 110 image .

[0074] To such purpose , the second subsystem may comprise a modal detector 112 configured to detect such far- field image .

[0075] For instance , the far- field image obtained by the modal detector 112 can be obtained via a first lens 114 used to observe the signal transmitted by the sample Obj in its Fourier plane , that is , in a second focal plane of such first lens 114 .

[0076] It is noted that i f the second system 10b i s considered, the noise pattern generated by the noise pattern generator 102ais a noise pattern known a pri ori .

[0077] I f either one of the first system 10aand the second system 10b is considered, the modulation of the transmission function of the sample Obj with a coherent mask may be represented with the following equation :

[0078] 7(x) = i4(x) ■ N(x) wherein :

[0079] A (x ) is a complex field transmittance function of the sample Obj , for instance , expressed as a function of a coordinate x of the plane of the sample Obj , wherein the coordinate x, while being a continuous coordinate as far as pure physics modelling is concerned, is generally processed as a discrete-values, pixel-based coordinate in preferred embodiments of the invention, for instance, corresponding to pixels of a light-sensitive sensor such as a camera configured to sense the signal transmitted by the sample object Obj;

[0080] V(x) is an electric field at the surface of the sample Obj, for instance, expressed as a function of a coordinate x of the plane of the sample Obj ; and

[0081] N(x) is a positive-valued stochastic variable related to the random noise, that is, indicating the random coherent intensity pattern projected on the sample Obj, for instance, expressed as a function of a coordinate x of the plane of the sample Obj and neglecting the uniform phase profile.

[0082] Assuming that the modal detector 112 and / or the spatial detector 120, that is, the imaging system, provide a point resolution, the operation of detecting the far-field image comprise measuring the following modal quantity:

[0083] IM = \F[A-N]^\2while the operation of detecting the corresponding nearfield image comprise measuring the following spatial quantity :

[0084] It is noted that if the second system 10b is considered, the spatial quantity is known since the noise pattern generated by the noise pattern generator 102ais a noise pattern known a priori, thus, in such a case, the spatial quantity may be equal to:

[0085] / s(x) = N(x)2

[0086] It is noted that the term F[g ( ) denotes a two- dimensional ("2D") spatial Fourier transformation of a generic function g, for instance, 24(X) -N(X), that is: where where k is a wavenumber of the light wave generated by the light source 106, f is a focal length of the first lens 114, and xkis a transverse coordinate in the second focal plane of the first lens 114.

[0087] Therefore, the far-field image detected via the modal detector 112 is an image signal related to the electric field V(x) of the surface of the sample Obj expressed in a modal domain, that is, in the Fourier

[0088] (frequency) domain, for instance, detected in a Fourier plane of a 4-f system described in the following, that is, in a space comprised between the first lens 114 and the second lens 118.

[0089] Hence, such far-field image is an image signal obtained in the modal domain, that is, in the Fourier (frequency) domain, as a function of the complex field transmittance function A(x) of the sample Obj and of the positive-valued stochastic variable N(x) related to the random noise.

[0090] The near-field image detected via the spatial detector 120 is an image signal related to the electric field V(x) of the surface of the sample Obj expressed in a spatial domain (that is, a state of a two-dimensional matrix where each element of the matrix represents pixel intensity) , that is, as a function of the coordinate x of the plane of the sample Obj, for instance, detected in a space following the second lens 118 of the 4-f system described in the following.

[0091] Hence, such near-field image is an image signal obtained in the spatial domain, as a function of the complex field transmittance function A(x) of the sample Obj and of the positive-valued stochastic variable N(x) related to the random noise. Information related to the sample Obj may be obtained by computing a covariance between the modal quantity and the spatial quantity ( either detected via the modal detector 112 and / or the spatial detector 120 or known) .

[0092] I f the first system 10ais considered, such covariance can be obtained with the following equation : cov[Is)Im(jc)] = EUS^I^K)] - E[ / s(x)]E[ / m(K)] where E[X] denotes the expectation value of a random stochastic variable X .

[0093] It is noted that both the modal quantity and the spatial quantity Isx) are stochastic quantities due to their dependence on the generated noise pattern N .

[0094] I f the second system 10b is considered, such covariance can be obtained with the following equation :

[0095] It is noted that a dataset obtained via either the first system 10aor the second system 10 may comprise a four-dimensional array wherein two axes are related to single- frequency images of the sample Obj , that is , the previously described far- field image in the frequency domain, and the other two axes are related to the spatial frequencies themselves , that is , the previously described near- field image in the spatial domain .

[0096] It is also noted that the image resulting from the superposition of the whole frequency content of the sample such as that obtained via conventional imaging devices can be obtained by superimposing, that is , by summing, the images corresponding to the single frequencies , that is , the near- field image and the far- field image .

[0097] Therefore , a dataset obtained via either the first system 10aor the second system 10b comprises more information related to the sample than a dataset obtained via conventional imaging systems . Di f ferently from conventional imaging devices , also in the case of an image resulting from the superposition of the whole frequency content of the sample it is possible to perform spatial filtering operations by excluding, for instance , entirely via software and without using any filtering hardware component , contributions from any unwanted spatial frequencies in order to obtain a filtered image containing only the spectral features of interest of the sample .

[0098] A first advantage of disposing of the filtering hardware component is that the user is allowed to perform a wider variety of operations on the sample without needing manipulation of the imaging device setup .

[0099] In addition to that , the costs for the production of the imaging device are reduce since the filtering hardware component are not requested .

[0100] For instance , a device that can be used to implement the intensity modulator 104 described above can be a digital micromirror device ("DMD" ) .

[0101] I f a digital micromirror device is used to generate coherent patterns , a measured noise pattern N (x ) can assume binary values in case of correspondence between pixels on the imaging sensor ( such as a camera or any other light-sensitive detector ) , micromirrors of the digital micromirror device , and resolution cells on the plane of the sample Obj , that is , in the event of a si zematching ( or quasi-matching) between the pixels on the imaging sensor, the micromirrors on the digital micromirror device , and the resolution cells on the plane of the sample Obj .

[0102] The wording "resolution cell" denotes the si ze o f the Airy disk on the sample Obj resulting from both the relay lens 108 and one or more imaging lenses , for instance , the first lens 114 and the second lens 118 , of the imaging system ( for instance , a camera ) , or else resulting from the sole relay lens 108 in the event of an imaging system (camera) with no imaging lens (for instance, as in the case of the second system 10B) .

[0103] The relay lens is configured to reproduce the array of micromirrors of the digital micromirror device on the sample Ob j , while the imaging lens is configured to reproduce the image of the sample Obj on the imaging sensor .

[0104] Therefore, in such a case, the statistical behaviour of the generated noise pattern N(x) obeys a Bernoulli distribution in every resolution cell.

[0105] For instance, it is possible to assume that high logic levels, that is, "1", and low logic levels, that is, "0", have a same probability of 0.5 in such distribution .

[0106] In addition, if the pixels of the spatial detector 120 are matched to the resolution cells, it is possible to consider the generated noise pattern N(x) as a discrete variable, for instance, a discrete two- dimensional array of independent random variables.

[0107] In such a way, if the first system 10ais considered, the covariance between the modal quantity and the spatial quantity is described by the following equation: while, if the second system 10b is considered, the following equation can be considered:

[0108] The correlation function, that is, the covariance between the modal quantity and the spatial quantity, can be used to isolate different features of the sample.

[0109] It is noted that the features described in the following are just exemplary features, therefore, also other features may be isolated starting from the correlation function described above.

[0110] In the following description, the first system 10a of Figure 1A is considered, but similar conclusions may also apply if the second system 10b of Figure IB is considered .

[0111] It is noted that by separating the complex field transmittance function A(x) of the sample Obj in its module and phase components, that is: it is possible to compute a normalized correlation function as:

[0112] The features of such normalized correlation function can allow to extract information related to the sample Obj, for instance, information related to the nature of the sample, for instance, related to a phase or non-phase nature of the sample, can be deduced from the parity of the normalized correlation function C (x, K) in the second variable K .

[0113] A phase object, that is, a sample characterized by a modulated value of the phase <>(x), is a sample that changes the phase of a light wave by having a non-uniform spatial distribution of refractive index or thickness, such phase object having a uniform amplitude transmission distribution so that it leads to small changes in the amplitude of the light waves and large changes in their phases.

[0114] A non-phase object is a sample characterized by a non-modulated value of the phase <>(x) , produces an even correlation function C(X,K) in the second variable K since the Fourier transform of a complex field transmittance function A(x) of a sample Obj having a real field transmittance has the following property: so that the non-phase normalized correlation function CWP(X,K) can be expressed as:

[0115] In view of the above , computing an integration of the non-phase normali zed correlation function CNP(X, K) over the second variable K is equivalent to an identity operation on the obj ect transmittance A:

[0116] J CNP(x, K')d.K = F-1[F[i4]](x) therefore , the field transmittance function A (x ) of the sample Obj can be reconstructed .

[0117] It is also noted that such non-phase normali zed correlation function CWP(X, K) is related to a richer amount of information regarding the sample Obj than a square root of the intensity that is measured by the spatial detector 120 .

[0118] In fact , it is possible to apply a modal filtering, that is , a filtering related to the frequency domain, to the non-phase normali zed correlation function CNP(x, K) in order to exclude unwanted spatial modes from the reconstruction of the complex field transmittance function A (x ) of the sample Obj .

[0119] Such modal filtering can be done by multiplying the non-phase normali zed correlation function CNP(x, K) for a filtering function FZZ(K) , obtaining, via integration over the second variable K , a filtered reconstruction Afnt (x ) of the complex field transmittance function A (x ) of the

[0120] It is noted that such modal filtering obtained by multiplying the non-phase normali zed correlation function CNP(x, K) for the filtering function FZZ(K) allows to obtain more information related to the sample Obj i f compared with a filtering technique comprising placing masks in the Fourier plane of the previously described 4- f system, that is , in the space comprised between the first lens 114 and the second lens 118 . In fact, if such a filtering technique is used to implement the filtering function FU(K in the Fourier plane, the filtered image of the field transmittance function A(x) of the sample Obj is:

[0121] Therefore, the filtered reconstruction Afut (x) contains more information related to the sample Obj if compared with the filtered image A'fiit(x) obtained using the masks in the Fourier plane since such filtered image A' fnt(x) is related to a square module of the filtered Fourier transform, whereas the filtered reconstruction Afnt(x) also provides information related to the real part of the function.

[0122] For instance, as a consequence of the above (see in particular Figure 3) , it is possible to obtain a same profile edge enhancing of that obtained via a high-pass filtering, but preserving information about whether the intensity gradient, that is, the change in the direction of the intensity level of an image, of the profile of the sample Obj is positive or negative.

[0123] To summarize, solutions as described herein are related to a method for performing spatial frequency- selective imaging of a sample Obj that can be either a phase object (as better described in the following, for instance, in the description of Figure 5) or a non-phase object, such method comprising: projecting a random coherent light pattern N(x) , that is, the random coherent intensity pattern, on the sample Obj, such random coherent light pattern N(x) comprising a coherent light wave, that is, the generated light wave, and a random noise pattern; obtaining a first image signal Is(x) and a second image signal as a function of a transmittance A(x) of the sample Obj and of such random coherent light pattern N(x) , such first image signal Is(x) being in a spatial domain x and such second image signal Zm( / <) being in a frequency domain K ; computing, for instance , via a processing unit , a correlation C (x, K) between such first image signal Is(x) and such second image signal Zm( / <) ; and filtering such correlation C(X, K) in the frequency domain K by applying a given frequency filtering function FZZ(K) thereto .

[0124] It is noted that the operation of applying the given frequency filtering function FZZ(K) to the correlation C (x, K) can be performed by multiplying such correlation C (X, K) for such given frequency filtering function FZZ(K) .

[0125] In embodiments , such method may further comprise obtaining a filtered transmittance ^^(x) of the sample Obj by integrating in the frequency domain K the computed correlation C (X, K) having applied thereto such given frequency filtering function FZZ(K) .

[0126] It is noted that such operation of proj ecting may be performed by : generating, via a light source 106 , preferably a laser source , such coherent light wave ; generating, via a noise pattern generator 102a, such random noise pattern; obtaining, via an intensity modulator 104 , preferably a digital micromirror device ("DMD" ) , the random coherent light pattern N (x ) based on such received coherent light wave and such received random noise pattern; and proj ecting, via such intensity modulator 104 , such random coherent light pattern N (x ) on such sample Obj , for instance , by sending such random coherent light pattern N (x ) to at least one relay lens 108 .

[0127] The operation of obtaining the first image signal Is(x) and the second image signal Zm( / <) may comprise : modulating such transmittance A (x ) of the sample Obj with such random coherent light pattern N (x ) , obtaining an electric field V (x ) of the sample Obj in the spatial domain x ; transposing, via a lens or via an optical assembly, for instance , via the first lens 114 , such electric field V (x ) from the spatial domain x to a frequency domain K , obtaining an electric field in the frequency domain K ; and detecting, via a modal detector 112 , such electric field in the frequency domain, obtaining such second image signal in the frequency domain .

[0128] It is noted that such operation may further comprise : i f such random noise pattern, for instance , generated via the noise pattern generator 102a, is known, computing the first image signal Is(x) based on such known random noise pattern; or i f such random noise pattern is unknown, detecting, via a spatial detector 120 , such electric field in the spatial domain x, obtaining such first image signal Is(x) in the spatial domain x .

[0129] In such second case , that is , i f such random noi se pattern is unknown, the operation of detecting, via the spatial detector 120 , such electric field in the spatial domain x may comprise : splitting, via a beam splitter 116 , such electric field in the frequency domain into a first electric field in the frequency domain and a second electric field in the frequency domain, such second electric field in the frequency domain being the electric field detected via the modal detector 112 ; and transposing, via a further lens or via a further optical assembly, for instance , via the second lens 118 , such first electric field from the frequency domain to the spatial domain, obtaining such electric field V(x) in the spatial domain x detected via the spatial detector 120.

[0130] In addition, it is noted that the method described herein may comprise: determining a parity of the computed correlation C(X,K) in the frequency domain K; and classifying, if the parity of the computed correlation C(X,K) in the frequency domain K is even, the sample Obj as a non-phase object, preferably reconstructing the transmittance A(x) of the sample Obj by integrating in the frequency domain K the computed correlation C (%, K) .

[0131] In embodiments of solutions as described herein, the previously described method may further comprise classifying, if the parity of the computed correlation C X,K) in the frequency domain K is odd, the sample Obj as a phase object.

[0132] Figures 2 and 3 illustrate exemplary reconstructions 20i, 202, and 2O3 of a double-slit (specifically a non-phase double slit) sample Obj obtained using the first system 10aof Figure 1A according to embodiments of the present description.

[0133] It is noted that even if the first system 10aof Figure 1A is considered, similar conclusions may also apply if the second system 10b of Figure IB is considered .

[0134] It is also noted that the double-slit sample Obj considered in the following is just an exemplary sample, therefore, also other samples may be considered and similar conclusions may apply also to such other samples.

[0135] For instance, the considered double-slit sample Obj may comprise: a first slit having a transmissivity that is higher than a transmissivity of a background of the sample, for instance, by 10%; and a second slit having a transmissivity that is higher than the transmissivity of the background of the sample , for instance , by 20% , and thus higher than the transmissivity of the first slit .

[0136] It is noted that the analysis of such a double-slit sample Obj may require DC filtering or dark- field imaging ( that is , an imaging technique which exclude unscattered beams from the image ) in order to improve the image visibility and signal-to-noise ratio .

[0137] It is noted that Figures 2 and 3 are related to a one-dimensional imaging in order to provide for an easier and more clear visuali zation of the Figures .

[0138] Instead, Figure 4 is related to a two-dimensional imaging of simulated cells assumed as non-phase obj ect .

[0139] It is noted that the correlation function, that is , the covariance between the modal quantity and the spatial quantity, shall be obtained by averaging over a plurality of frames , for instance , over about 106frames .

[0140] The unit of measurement for the y-axis of each plot in Figure 2 and 3 is an essentially arbitrary unit "AU" resulting from the normali zed correlation function set forth in the foregoing : the unit of measurement is reminiscent of that of an intens ity, however - in view of the function - is does not strictly correspond to

[0141] Figure 2 illustrates , in a first subfigure 2 0A, a reconstruction R related to a non-phase double-slit sample Ob , that is , the reconstruction of the complex field transmittance function A (x ) , as that described above obtained using the first system 10aof Figure 1A, such reconstruction R being represented as a function of a spatial coordinate x of the plane of the sample Obj .

[0142] Figure 2 illustrates , in a second subfigure 2 0B , the reconstruction R of the first subfigure 2 0A having applied thereto operations to improve the contrast and the signal-to-noise ratio ("SNR" ) , obtaining a DC- filtered reconstruction DCf as a function of the spatial coordinate x .

[0143] For instance , such operations for improving the contrast and the signal-to-noise ratio may be performed by zeroing-out the values corresponding to the DC component in the modal coordinate , that is , the center ( or zero ) of the second variable K , of the normali zed correlation function C (x, K) before summing over such second variable K . In this latter regard, ^^(x) is defined as an integral , which corresponds to the summing operation referred to above in view of the discrete processing (pixel-based) of the x variable (which is otherwise continuous in the pure physical model ) in embodiments of solutions as described herein .

[0144] Figure 3 illustrates , in a first subfigure 20c, the reconstruction R of the first subfigure 2 0A having applied thereto : a zeroing-out operation of the values corresponding to the DC component and first harmonic components in the modal coordinate , that is , in the space related to the second variable K ; and an integration operation over such second variable K , obtaining a HP- fi ltered reconstruction HPf as a function of the spatial coordinate x .

[0145] It is noted that the positive and negative discontinuities in the HP- filtered reconstruction HPf can allow the identi fication of the sign of the changes in the intensity gradients .

[0146] Figure 3 illustrates , in a second subfigure 2 0D , the non-phase double-slit sample Obj and a reference performance FF_HPf of the conventional filtering technique comprising placing masks ( that is , additional hardware components ) in the Fourier plane of the previously described 4- f system using a second harmonic high-pass ("HP" ) filtering, such reference performance being represented as a function of the spatial coordinate x .

[0147] In such subfigure 2 0D it is possible to note the typical edge enhancement of the high-pass filtering at the expenses of qualitative and / or quantitative information related to the intensity distribution .

[0148] It is noted that the filtering proposed herein, that is , the modal filtering done by multiplying the nonphase normali zed correlation function CWP(X, K) for a filtering function FIZ(K) , obtaining, via integration, a filtered reconstruction Afnt (x ) , may allow to obtain such edge enhancement without losing qualitative and / or quantitative information related to the intensity distribution .

[0149] Such advantage of the filtering proposed herein can be observed from Figure 3 , which illustrates , in a third subfigure 2 0E , the absolute value of the HP- filtered reconstruction HPf indicated with the reference Vali and the square root of the reference performance FF_HPf indicated with the reference Val2 , both represented as a function of the spatial coordinate x .

[0150] Coordinate y is displayed in association to a unit of measurement labelled "AU" , i . e . an essentially arbitrary unit as explained above .

[0151] Such third subfigure 2 0E aims at demonstrating that the squared absolute value of the HP- filtered reconstruction HPf has a behaviour equal to the reference performance FF_HPf illustrated in the second subfigure 2 0D of Figure 3 obtained using the conventional filtering technique comprising placing masks in the Fourier plane of the 4- f system . Therefore, demonstrating that the amount of information comprised in the HP-filtered reconstruction HPf described herein is larger than that comprised in the reference performance FF_HPf of the conventional filtering technique comprising placing masks in the Fourier plane of the 4-f system.

[0152] Figure 4 illustrates, in a first subfigure 20F, a two-dimensional reference intensity profile of the sample Obj obtained by performing a summing operation without applying thereto a filtering function FIZ(K), that is, via the identity operation described above.

[0153] Figure 4 illustrates, in a second subfigure 20G, a square root, for instance, for dynamic range compression, of a two-dimensional HP-filtered (that is, high-pass filtered) reconstruction. Such HP-filtered reconstruction can be obtained using the previously described equation of the filtered reconstruction Afnt(x) and using as filtering function FU(K a high-pass HP filter having a filtering threshold defined according to a desired edge enhancement, thus, filtering the low frequencies until the desired edge enhancement has been obtained .

[0154] It is noted that such two-dimensional HP-filtered reconstruction may allow to identify the profiles of the simulated cells as conventionally done via dark-field imaging, thus, showing more contrast and details inside the cells with respect to the result obtained in the first subfigure 20F using the identity operation. Therefore, the second subfigure 20G shows the advantages of applying a filtering function FU(K to the reconstruction with respect to obtaining such reconstruction via the identity operation used in the first subfigure 20F.

[0155] Figure 4 illustrates, in a third subfigure 20H, a two-dimensional slice extracted from a four-dimensional normalized correlation function C (x, K) , showing, since the sample Obj is a non-phase object, an expected parity in the second variable K, that is, along the horizontal axis of the third subfigure 20H.

[0156] It is noted that the y-axis, that is, expressed in a coordinate y, and the x-axis, that is, expressed in the previously described coordinate x, of the first subfigure 20F and the second subfigure 20G of Figure 4 are both expressed via coordinates that while being continuous as far as pure physics modelling is concerned, are generally processed as discrete-values, pixel-based coordinates in preferred embodiments of the invention, for instance, corresponding to pixels of a lightsensitive sensor such as a camera used to sense the signal transmitted by the sample object Obj .

[0157] Differently, the y-axis of the third subfigure 20H is expressed via the previously described coordinate x and the x-axis is expressed by multiplying such coordinate x for the second variable K, that is, KX .

[0158] It is also noted that the grey scale of the subfigures of Figure 4 is expressed in the previously described arbitrary unit "AU".

[0159] It is noted that the normalized correlation function C (x, K) may comprise useful information also when the sample Obj is a phase object, that is, a sample characterized by almost completely flat intensity profile but having a modulated phase profile in space.

[0160] Typically, given that such type of samples, that is, phase objects, are difficult to image with conventional imaging devices, known solutions analyses such samples using techniques configured to enhance phase contrast.

[0161] If the complex field transmittance function A(x) of the sample Obj is pure-phase, that is, in the form an integration over the modal information, that is , over the second variable K, of the normali zed correlation function C (x, K) yields to a flat profile , obtaining : as is the case for conventional phase-insensitive imaging techniques .

[0162] It is noted that , even i f the sample Obj has a flat intensity, its features can be extracted using the previously described correlation function having applied thereto a filtering function FZZ(K) , therefore , not only by simply integrating over K but also by applying such filtering function FZZ(K) to the correlation .

[0163] Figure 5 illustrates exemplary phase-contrast microscopy 30 of a pure-phase sample , that is , a pure- phase obj ect , Obj according to embodiments of the present description, such phase-contrast microscopy being obtained by filtering out the DC component in the modal plane .

[0164] It is noted that such phase-contrast microscopy shall be obtained by simulating a plurality of frames , for instance , about 105frames , and then evaluating the normali zed correlation function C (x, K) .

[0165] Figure 5 illustrates , in a first subfigure 30A, a phase profile < >(x) = arg (?l(x)) of the sample Obj . Modulation to the phase profile is obtained under the assumption that the refractive index is affected by the presence of capillaries in a biological tissue .

[0166] Figure 5 illustrates , in a second subfigure 30B, an integration over the modal information, that is , over the second variable K, of the normali zed correlation function C (X, K) , yielding, as expected from the correlation image of a phase obj ect , to an image that is flat and dominated by noise due to the high transmissivity of the sample .

[0167] Figure 5 illustrates , in a third subfigure 30c, a two-dimensional s lice extracted from a four-dimensional normali zed correlation function C (x, K) , showing a plurality of combined spatial-modal information even in presence of a pure-phase obj ect . Such correlation function can be regarded as an even function of K (hori zontal coordinate ) .

[0168] Figure 5 illustrates , in a fourth subfigure 30D , a reconstruction of a phase profile of the phase sample Obj obtained by filtering out the DC component and summing over all the modes .

[0169] It is noted that the y-axis , that is , expressed in the previously described coordinate y, and the x-axis , that is , expressed in the previously described coordinate x, of the first subfigure 30A, second subfigure 30B , and fourth subfigure 30D of Figure 5 are both expressed via coordinates that while being continuous as far as pure physics modelling is concerned, are generally processed as discrete-values , pixel-based coordinates in preferred embodiments of the invention, for instance , corresponding to pixels o f a lightsensitive sensor such as a camera used to sense the signal transmitted by the sample obj ect Obj .

[0170] Di f ferently, the y-axis of the third subfigure 30c is expressed via the previously described coordinate x and the x-axis is expressed by multiplying such coordinate x for the second variable K , that is , KX .

[0171] It is also noted that the grey scale of the first subfigure 30A is expressed in a scale from zero to a value equal to pi ( that is , TT ) PI while the grey scale of the other subfigures of Figure 5 is expressed in the previously described arbitrary unit "AU" .

[0172] Therefore , solutions as described herein facilitate achieving a method for performing spatial frequency- selective imaging of a sample Obj , such sample being either a phase sample or a non-phase sample . The method described herein comprises: projecting, for instance, via the intensity modulator 104, a random coherent light pattern, that is, the previously described random coherent intensity pattern, on the sample Ob , such random coherent light pattern comprising a coherent light wave, for instance, a light plain wave generated by the light source 106, and a random noise pattern, for instance, generated by the noise pattern generator 102a; obtaining a first image signal Is(x) , that is, the near-filed image obtained via the spatial detector 120, and a second image signal Im(K) , that is, the far- field image obtained via the modal detector 112, as a function of a transmittance A(x) of the sample Ob and of such random coherent light pattern N(x) , such first image signal being in a spatial domain and such second image signal being in a frequency domain; computing a correlation C (x, K) between such first image signal Is(x) and such second image signal Im (K) ; and filtering such correlation C (x, K) in the frequency domain K by applying a given frequency filtering function Fil (K) thereto.

[0173] In various embodiments of the present description, the operation of applying the given frequency filtering function Fil (K) to the correlation C (x, K) may be performed by multiplying such correlation C (x, K) for such given frequency filtering function Fil (K) .

[0174] Solutions as described herein also refer to a system, for instance, the first system 10aor the second system 10b, for performing spatial frequency-selective imaging of a sample Ob , such sample being either a phase sample or a non-phase sample. However, the above are only examples discussed herein out of the plurality of possibly examples. In other terms, the sample does not need to fall strictly under either of the above circumstances. Most samples are in fact both modulated in phase and in intensity, so they have a combination of phase and non-phase features.

[0175] The system 10aor 10b as described herein comprises: a first subsystem configured to project, for instance, via the intensity modulator 104, a random coherent light pattern on the sample Ob , that is, the previously described random coherent intensity pattern, such random coherent light pattern comprising a coherent light wave, for instance, a light plain wave generated by the light source 106, and a random noise pattern, for instance, generated by the noise pattern generator 102a; a second subsystem configured to obtain a first image signal Is(x) , that is, the near-filed image obtained via the spatial detector 120, and a second image signal Im(K) , that is, the far-field image obtained via the modal detector 112, as a function of a transmittance A(x) of the sample Ob and of such random coherent light pattern N(x) , such first image signal Is(x) being in a spatial domain and such second image signal Im(K) being in a frequency domain; and a processing unit, for instance, a processing unit comprised in the first system 10aor in the second system 10b, configured to compute a correlation C (x, K) between such first image signal Is(x) and such second image signal Im(K) , and to filter such correlation C (x, K) in the frequency domain K by applying a given frequency filtering function Fil (K) thereto.

[0176] In various embodiments of the present description, the operation of applying the given frequency filtering function Fil (K) to the correlation C (x, K) may be performed by multiplying such correlation C (x, K) for such given frequency filtering function Fil (K) .

[0177] In embodiments according to the present description, the processing unit may be configured to obtain a filtered transmittance Afnt(x) of the sample Obj by integrating in the frequency domain K the computed correlation C (x, K) having applied thereto such given frequency filtering function Fil (K) .

[0178] In embodiments according to the present description, the first subsystem may comprise: a light source 106, preferably a laser source, configured to generate the coherent light wave; a noise pattern generator 102aconfigured to generate such random noise pattern; and an intensity modulator 104, for instance a digital micromirror device ("DMD") , configured to receive such coherent light wave and such random noise pattern, to obtain the random coherent light pattern N(x) based on such received coherent light wave and such received random noise pattern, and to project such random coherent light pattern N(x) on such sample Obj .

[0179] It is noted that the first system 10aor the second system 10b may comprise at least one relay lens 108 or a relay optical assembly, that is, either a single lens or a set of lenses, and the operation of projecting, for instance, via the intensity modulator 104, the random coherent light pattern N(x) on the sample Obj may be performed by sending such random coherent light pattern N(x) to such at least one relay lens 108.

[0180] In solutions as described herein, such second subsystem may comprise: a lens or an optical assembly, for instance, the first lens 114, configured to transpose an electric field V(x) of the sample Obj, obtained by modulating such transmittance A(x) of the sample Obj with such random coherent light pattern N(x) , from the spatial domain to a frequency domain, obtaining an electric field in the frequency domain; and a modal detector 112 configured to detect such electric field in the frequency domain, obtaining such second image signal Im(K ) in the frequency domain K .

[0181] In addition, solutions as described herein may comprise : i f such random noise pattern is known, for instance , as in the case of the second system 10b, the processing unit of such system may be configured to compute the first image signal Is(x ) based on such known random noise pattern; or i f such random noise pattern is unknown, for instance , as in the case of the first system 10a, such first system 10amay comprise a spatial detector 120 configured to detect such electric field in the spatial domain, obtaining such first image signal Is(x ) in the spatial domain .

[0182] I f such random noise pattern is unknown, such first system 10amay further comprise : a beam splitter 116 configured to split such electric field in the frequency domain into a first electric field in the frequency domain and a second electric field in the frequency domain, such second electric field in the frequency domain being the electric field detected via the modal detector 112 ; and a further lens or a further optical assembly, for instance , the second lens 118 , configured to transpose such first electric field from the frequency domain to the spatial domain, obtaining such electric field in the spatial domain detected via the spatial detector 120 .

[0183] In embodiments according to the present description, the processing unit may be configured to : determine a parity of the computed correlation C (x, K ) in the frequency domain, that is , in the second variable K ; and classi fy, i f the parity of the computed correlation C (x, K ) in the frequency domain K is even, the sample Obj as a non-phase obj ect , preferably reconstructing the transmittance A (x ) of the sample Obj by integrating in the frequency domain K the computed correlation C (x, K ) .

[0184] In embodiments according to the present description, such processing unit may be further configured to classi fy, i f the parity of the computed correlation C (x, K ) in the frequency domain K is odd, the sample Obj as a phase obj ect .

[0185] In addition, solutions as described herein also refer to a computer program product loadable in a memory of a system as described herein, for instance , a memory of a processing unit comprised in either the first system 10aor the second system 10b, and comprising portions of software code for executing the phases of the method according to the present description .

[0186] Thus , solutions as described herein facilitate obtaining a capability of filtering ( either by selecting or by removing) spatial frequency content from an image o f a s amp 1 e .

[0187] Such filtering capability can be advantageous , for instance , in industrial inspection and quality control applications .

[0188] In such applications , the features of a sample , whether desired or unwanted, can be associated to a speci fic range of frequencies of the Fourier domain, so that their presence can be asserted by applying a modal filtering according to the present description, for instance , a band-pass or a band-cut filter, related to the spectral region, that is , to the range of frequencies , of interest .

[0189] In addition, as previously described, solutions as described herein may also be used with phase obj ects , for instance , thin transparent films , wherein a higher contrast compared to results obtained via conventional imaging can be obtained through the previously described modal filtering operations , as can also be seen in Figure 5 .

[0190] In fact , it is noted that even phase defects such as local variations of the refractive index and scratches , which are di fficult to identi fy via conventional imaging, can be associated to recogni zable fingerprints in the frequency domain, that is , to speci fic ranges of frequencies , so that they can be isolated with high contrast using a band-pass filtering related to the range of frequencies of interest ( that is , related to the phase defects that are to be detected) .

[0191] Even i f the present document is focused on industrial inspection and quality control applications , solutions as described herein can be used in every imaging context wherein a spatial frequency filtering yields advantages in the quality and / or features of the final image .

[0192] For instance , other applications may be related to dark- field imaging and microscopy, Fourier filtering, high-pass and low-pass filtered imaging, phase-sens itive imaging, and the like , in several fields , for instance , from biomedical applications to industrial , spatial , and semiconductor ones .

[0193] Without prej udice to the underlying principles , the details and the embodiments may vary, even signi ficantly, with respect to what has been described by way of example only without departing from the scope of the embodiments .

[0194] The extent of protection is determined by the annexed claims .

Claims

CLAIMS1. Method for performing spatial mode-selective imaging of a sample (Obj) , said method comprising: projecting (104) a random coherent light pattern on the sample (Obj) , said random coherent light pattern comprising a coherent light wave (106) and a random noise pattern (102a) ; obtaining a first image signal (120) and a second image signal (112) as a function of a transmittance of the sample (Obj) and of said random coherent light pattern, said first image signal (120) being in a spatial domain and said second image signal (112) being in a frequency domain; computing a correlation, preferably a covariance, between said first image signal and said second image signal; and filtering said correlation in the frequency domain by applying a given frequency filtering function thereto, preferably wherein said operation of applying the given frequency filtering function to the correlation is performed by multiplying said correlation for said given frequency filtering function.

2. The method according to claim 1, wherein said method comprises obtaining a filtered transmittance of the sample (Obj) by integrating in the frequency domain the computed correlation having applied thereto said given frequency filtering function.

3. The method according to claim 1 or claim 2, wherein the operation of projecting is performed by: generating, via a light source (106) , preferably a laser source, said coherent light wave; generating, via a noise pattern generator(102a) , said random noise pattern; obtaining, via an intensity modulator (104) , preferably a digital micromirror device, the random coherent light pattern based on said received coherent light wave and said received random noise pattern; and projecting, via said intensity modulator (104) , said random coherent light pattern on said sample (Obj) , preferably by sending said random coherent light pattern to at least one relay lens or to a relay optical ass e mb 1 y (108) .

4. The method according to any one of the previous claims, wherein the operation of obtaining the first image signal (120) and the second image signal (112) comprises : modulating said transmittance of the sample (Obj) with said random coherent light pattern, obtaining an electric field of the sample (Obj) in the spatial domain; transposing, via a lens or an optical assembly (114) , said electric field from the spatial domain to a frequency domain, obtaining an electric field in the frequency domain; and detecting, via a modal detector (112) , said electric field in the frequency domain, obtaining said second image signal (112) in the frequency domain.

5. The method according to claim 4, wherein the operation of obtaining the first image signal (120) and the second image signal (112) further comprises: if said random noise pattern is known, computing the first image signal (120) based on said known random noise pattern; or if said random noise pattern is unknown, detecting, via a spatial detector (120) , said electricfield in the spatial domain, obtaining said first image signal (120) in the spatial domain.

6. The method according to claim 5, wherein if said random noise pattern is unknown, said operation of detecting, via the spatial detector (120) , said electric field in the spatial domain comprises: splitting, via a beam splitter (116) , said electric field in the frequency domain into a first electric field in the frequency domain and a second electric field in the frequency domain, said second electric field in the frequency domain being the electric field detected via the modal detector (112) ; and transposing, via a further lens or a further optical assembly (118) , said first electric field from the frequency domain to the spatial domain, obtaining said electric field in the spatial domain detected via the spatial detector (120) .

7. The method according to any one of the previous claims, wherein said method comprises: determining a parity of the computed correlation in the frequency domain; and classifying, if the parity of the computed correlation in the frequency domain is even, the sample (Obj) as a non-phase object, preferably reconstructing the transmittance of the sample (Obj) by integrating in the frequency domain the computed correlation; preferably wherein said method further comprises classifying, if the parity of the computed correlation in the frequency domain is odd, the sample (Obj) as a phase object.

8. System (10a; 10b) for performing spatial mode- selective imaging of a sample (Obj) , said system (10a;10b) comprising: a first subsystem (104; 106; 102a) configured to project (104) a random coherent light pattern on the sample (Obj) , said random coherent light pattern comprising a coherent light wave (106) and a random noise pattern ( 102a) ; a second subsystem (112-120) configured to obtain a first image signal (120) and a second image signal (112) as a function of a transmittance of the sample (Obj) and of said random coherent light pattern, said first image signal (120) being in a spatial domain and said second image signal (112) being in a frequency domain; and a processing unit configured to compute a correlation between said first image signal and said second image signal, and to filter said correlation in the frequency domain by applying a given frequency filtering function thereto, preferably wherein said operation of applying the given frequency filtering function to the correlation is performed by multiplying said correlation for said given frequency filtering function .

9. The system (10a; 10 ) according to claim 8, wherein said processing unit is configured to obtain a filtered transmittance of the sample (Obj) by integrating in the frequency domain the computed correlation having applied thereto said given frequency filtering function.

10. The system (10a; 10b) according to claim 8 or claim 9, wherein said first subsystem (104; 106; 102a) comprises : a light source (106) , preferably a laser source, configured to generate said coherent light wave;a noise pattern generator (102a) configured to generate said random noise pattern; and an intensity modulator (104) , preferably a digital micromirror device, configured to receive said coherent light wave and said random noise pattern, to obtain the random coherent light pattern based on said received coherent light wave and said received random noise pattern, and to project said random coherent light pattern on said sample (Obj) , preferably wherein said system (10a; 10b) comprises at least one relay lens or a relay optical assembly (108) and the operation of projecting (104) the random coherent light pattern on the sample (Obj) is performed by sending said random coherent light pattern to said at least one relay lens or to said relay optical assembly (108) .

11. The system (10a; 10 ) according to any one of claims 8 to 10, wherein said second subsystem (112-120) comprises : a lens or an optical assembly (114) configured to transpose an electric field of the sample (Obj) , obtained by modulating said transmittance of the sample (Obj) with said random coherent light pattern, from the spatial domain to a frequency domain, obtaining an electric field in the frequency domain; and a modal detector (112) configured to detect said electric field in the frequency domain, obtaining said second image signal (112) in the frequency domain.

12. The system (10a; 10b) according to claim 11, wherein : if said random noise pattern is known (10b) , the processing unit is configured to compute the first image signal (120) based on said known random noise pattern; orif said random noise pattern is unknown, said system (10a) comprises a spatial detector (120) configured to detect said electric field in the spatial domain, obtaining said first image signal (120) in the spatial domain.

13. The system (10a; 10b) according to claim 12, wherein if said random noise pattern is unknown, said system (10a) comprises: a beam splitter (116) configured to split said electric field in the frequency domain into a first electric field in the frequency domain and a second electric field in the frequency domain, said second electric field in the frequency domain being the electric field detected via the modal detector (112) ; and a further lens or a further optical assembly (118) configured to transpose said first electric field from the frequency domain to the spatial domain, obtaining said electric field in the spatial domain detected via the spatial detector (120) .

14. The system (10a; 10 ) according to any one of claims 8 to 13, wherein the processing unit is configured to : determine a parity of the computed correlation in the frequency domain; and classify, if the parity of the computed correlation in the frequency domain is even, the sample (Obj) as a non-phase object, preferably reconstructing the transmittance of the sample (Obj) by integrating in the frequency domain the computed correlation; preferably wherein said processing unit is further configured to classify, if the parity of the computed correlation in the frequency domain is odd, the sample (Obj) as a phase object.15 . Computer program product loadable in a memory of the system according to any one of claims 8 to 14 and comprising portions of software code for executing the phases of the method of any one of the claims 1 to 7 .

Images