Digital imaging device and method for generating a digital color image - Patents.com

Abstract

To provide a digital imaging device and a method for generating a digital color image of an object based on a digital grayscale image.SOLUTION: In a fluorescence microscope, a digital imaging device has a processor. The processor acquires multiple digital grayscale images of an object with different spectral sensitivities. Each digital grayscale image contains grayscale information based on each one of the different spectral sensitivities. Multiple weighting factors for each digital grayscale image are acquired. The weighting factors are assigned to multiple color channels that define a predetermined color space. By distributing the grayscale information of respective grayscale images on the color channels according to the weighting factors, a digital color image of the object is synthesized from the digital grayscale images into the color space.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a digital imaging device, a microscope including a digital imaging device, and a method for producing a digital color image of an object. [Background technology]

[0002] In the field of microscopy, color cameras can be used to acquire images. Typically, such color cameras include a color filter array arranged on a grid of photosensors. In particular, so-called Bayer filter mosaics can be used as filter arrays for color imaging. Bayer filter mosaics include green (G), red (R) and blue (B) color filters in a given ratio, typically G 50%, R 25% and B 25%.

[0003] The use of color cameras in microscopy allows the detection of the actual color of objects, which is useful for evaluating samples in, for example, pathology and histology, and allows the determination of the staining of samples prior to detailed examination. However, the incorporation of color cameras into wide-field or confocal microscopes is often very costly. Therefore, monochrome imaging remains the preferred method in microscopy. Although it is conceivable to provide a microscope with both monochrome and color cameras, it is difficult to achieve accurate time and space matching of image data produced by multiple different imaging modalities.

[0004] The paper "Color image acquisition using monochrome camera and standard fluorescence filter cubes", GF Weber and AS Menko, Biotechniques, Vol. 38, No. 1, discloses a method for combining multiple monochrome images by graphic software. However, the method is not suitable for automatically creating a color image based on the monochrome imaging. Summary of the Invention [Problem to be solved by the invention]

[0005] It is an object to provide a digital imaging device and method that enables in a reliable manner to generate a digital colour image of an object on the basis of a digital greyscale image. [Means for solving the problem]

[0006] The microscope includes a digital imaging device including a processor configured to acquire a plurality of digital grayscale images of an object at a plurality of respective different spectral sensitivities, each digital grayscale image including grayscale information based on a respective one of the respective different spectral sensitivities. The processor is configured to acquire a plurality of weighting factors for each digital grayscale image, the weighting factors being assigned to a plurality of color channels defining a predetermined color space. The processor is further configured to synthesize a digital color image of the object from the digital grayscale images into the color space by distributing the grayscale information of each grayscale image onto the color channels according to the weighting factors.

[0007] The microscope can be used as a virtual color camera by converting a plurality of spectral input channels, each represented by a different spectral sensitivity of a plurality of digital grayscale images, into a plurality of spectral output channels provided by color channels that define the color space of the color camera to be simulated by the digital imaging device. Thus, each grayscale image belonging to one of the input channels, each represented by a different spectral sensitivity, is weighted with respect to a model of the color camera to be simulated by the digital imaging device.

[0008] The color space to which the digital grayscale image is converted can be one of the typical color spaces used in the field, such as RGB (red, green, blue), CMYK (cyan, magenta, yellow, black), etc.

[0009] Preferably, the number of spectral sensitivities of the grayscale image is different from the number of color channels of the color space, in particular, is greater than the number of color channels of the color space. The more input channels represented by the different spectral sensitivities of the grayscale images, the more image information can be used to create a digital color image. Thus, the quality of the digital color image increases with the number of spectral sensitivities of the grayscale images. The color imaging disclosed herein can be achieved regardless of whether the input spectra represented by the different spectral sensitivities of the grayscale images overlap. Furthermore, input spectra outside the visible range, such as infrared (IR) light, can be processed.

[0010] The weighting coefficients can be represented by a matrix, the number of rows of which is equal to the number of spectral input channels, i.e., the number of digital grayscale images, and the number of columns of which is equal to the number of output channels, i.e., the number of color channels of the camera model to be simulated. Thus, the determination of a digital color image based on multiple different digital grayscale images reduces to a simple matrix multiplication, which facilitates color imaging in real time.

[0011] The weighting coefficients based on which the grayscale information of each digital grayscale image is distributed onto the different color channels of the camera model can be determined in advance before acquiring the digital grayscale images, so that the computational effort required to synthesize a digital color image is small and real-time color imaging can be easily achieved.

[0012] Preferably, the grayscale information of each digital grayscale image is corrected for the quantum yield of the sensor, which is a device parameter that is known in advance. Therefore, when determining the weighting coefficients, it is already possible to take the quantum yield of the sensor into account. Therefore, the correction for the quantum yield of the sensor does not affect the color imaging in real time.

[0013] The weighting coefficients can be adjusted according to a Bayer filter, for example if an RGB camera model is used, the Bayer filter adjustment gives a greater weight to the G component with a value of 0.5 than the R and B components with values ​​of 0.25 each.

[0014] The processor can be configured to determine a maximum value among all image values ​​of the digital color image and normalize the image values ​​based on said maximum value. For example, if the digital color image is to be processed in the form of an 8-bit image containing values ​​from 0 to 256, a scale factor 256 / Max is calculated, where Max denotes the aforementioned maximum value, and all image values ​​of the digital color image are normalized by said scale factor.

[0015] The processor may further be configured to perform white balancing on the digital color image, thereby improving the quality of the digital color image.

[0016] According to a preferred embodiment, the microscope includes a plurality of image sensors, which are configured to acquire said plurality of digital grayscale images with said plurality of different spectral sensitivities. Thus, the plurality of grayscale images can be acquired sequentially or simultaneously by the plurality of image sensors, each having a different sensitivity in a spectral region. However, the digital imaging device is not limited thereto. For example, the digital imaging device can also be used to post-process grayscale images created by a single image sensor and a plurality of color filters, which are sequentially introduced into the optical path leading to the image sensor, to create a sequence of a plurality of grayscale images of a plurality of different spectra. Since the spectral characteristics of the color filters are known in advance, it is possible to determine the corresponding weighting coefficients, and a post-processing process can be performed on the sequence of the plurality of grayscale images to synthesize a digital color image.

[0017] Each of the image sensors can be formed by, but is not limited to, a wide field sensor. For example, the digital imaging device may be used in a confocal microscope using a point sensor to sequentially acquire digital grayscale images pixel by pixel.

[0018] Preferably, the microscope includes a display device configured to display the digital color image in real time.

[0019] Preferably, the microscope may be a confocal microscope or a wide-field microscope as already mentioned above, but is not limited thereto.

[0020] According to another aspect, there is provided a method for generating a digital color image of an object, the method comprising: acquiring, using a microscope, a plurality of digital grayscale images of the object at a plurality of respective different spectral sensitivities, each digital grayscale image including grayscale information based on a respective one of said respective different spectral sensitivities; acquiring a plurality of weighting factors for each digital grayscale image, said weighting factors being assigned to a plurality of color channels defining a predetermined color space; and synthesizing a digital color image of the object from the digital grayscale images into said color space by distributing the grayscale information of each grayscale image onto the color channels in accordance with the weighting factors.

[0021] For example, the weighting coefficients can be obtained by reading them from a memory. Thus, the weighting coefficients do not need to be determined during the imaging process. Rather, since both the spectral sensitivities of the digital grayscale image and the color channels of the camera model being utilized are known in advance, the weighting coefficients can be determined and stored in a memory prior to the imaging process. The memory can then be accessed to retrieve the weighting coefficients when acquiring an image.

[0022] According to another aspect, there is provided a computer program having a program code for carrying out the method.

[0023] Specific embodiments are described below with reference to the drawings. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a block diagram illustrating one embodiment of a digital imaging device. [Diagram 2] FIG. 1 is a schematic diagram illustrating a process performed by a digital imaging device to generate a digital color image. [Diagram 3]FIG. 1 is a schematic diagram of a system configured to carry out the methods described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] FIG. 1 illustrates a fluorescence microscope including a digital imaging device according to one embodiment.

[0026] FIG. 2 illustrates a process for synthesizing a digital color image performed by the digital imaging device shown in FIG.

[0027] FIG. 1 is a block diagram illustrating a fluorescence microscope 100, which may include an object slide 102 on which an object 104 is placed. The microscope 100 includes a digital imaging device 106 configured to generate a plurality of digital grayscale images of the object 104 with a plurality of different spectral sensitivities. To this end, the digital imaging device 106 may include a corresponding number of image sensors 108, 110, 112, and 114. Each of the image sensors 108-114 acquires a digital grayscale image, which are referred to as I1, I2, I3, and I4 in FIG. 1. The image sensors 108-114 may be formed by, but are not limited to, CMOS sensors. To acquire the plurality of grayscale images I1-I4 with different spectra, appropriate optical filters (not shown in FIG. 1) may be used, each of which transmits only a specific wavelength range of the fluorescent light emerging from the object 104 towards the image sensor.

[0028] The digital imaging device 106 further includes a processor 116 that is configurable to control the overall operation of the fluorescence microscope 100. In particular, the processor 116 is used to control the color imaging process as described in more detail below with reference to Figure 2. To this end, the processor 116 is coupled to the image sensors 108-114 to acquire digital grayscale images I1-I4 to be processed for color imaging.

[0029] The imaging device 106 may further include a display device 118 and a memory 120 similarly coupled to the processor 116 .

[0030] In the following, it can be assumed that the fluorescence microscope 100 is formed by a wide-field microscope, with each of the image sensors 108-114 acquiring a wide-field image of the object 104. However, the fluorescence microscope 100 is not limited to a wide-field configuration. For example, a confocal microscope configuration may be used, in which each image sensor 108-114 is formed by a point sensor, and each digital grayscale image I1-I4 comprises a number of pixels that are acquired sequentially by each image sensor 108-114.

[0031] Fig. 2 illustrates a process performed by the digital imaging device 106 for synthesizing a digital color image of the object 104 based on the digital grayscale images I1-I4 acquired by the image sensors 108-114. In the following example, it is assumed that the digital color image is an RGB image corresponding to the superposition of three color images of R, G and B. It goes without saying that the process illustrated in Fig. 2 may also be applied to other color spaces, such as for example the CMYK color space.

[0032] First, the processor 116 obtains normalized data representing the different spectral sensitivities of the image sensors 108-114, as shown by block B1 and diagram D1. Specifically, the image sensor 108 may have a spectral sensitivity as shown by curve S1 of diagram D1. Similarly, the spectral sensitivity of the image sensor 110 is shown by curve S2, the spectral sensitivity of the image sensor 112 is shown by curve S3, and the spectral sensitivity of the image sensor 114 is shown by curve S4. As can be seen in diagram D1, the spectral wavelength regions corresponding to the spectral sensitivities S1-S4 are substantially separated from each other, i.e., do not overlap. According to diagram D1 of FIG. 2, the spectral sensitivities S1, S2, S3, S4 may correspond substantially to blue, green, yellow, and red, respectively. As described below, the spectral sensitivities S1-S4 define four input channels to be converted into a color space of RGB space to simulate an RGB color camera.

[0033] Subsequently, a normalized quantum yield Y of each image sensor 108-114 is obtained, as shown by block B2 and diagram D2 in Fig. 2. The spectral sensitivities S1-S4 of the image sensors 108-114 are multiplied by the quantum yield Y to obtain spectral sensitivities S1a, S2a, S3a and S4a corrected for quantum yield, as shown by block B3 and diagram D3.

[0034] In the next step, the normalized model sensitivities SB, SG and SR of the RGB camera model are considered, as shown in block B4 and diagram D4. Using the RGB color space as the target color space, the model sensitivities SB, SG and SR are typically determined according to a Bayer filter. Specifically, the three spectral model sensitivities SB, SG and SR define a target model, onto which the spectral sensitivities S1, S2, S3, S4 of the image sensors 108-114 are mapped. In diagram D4, the model sensitivity SB corresponds to color B, the model sensitivity SG corresponds to color G, and the model sensitivity SR corresponds to color R. As a characteristic of the Bayer filter, the spectral model characteristic SG corresponding to G is overestimated in relation to the model sensitivities SB and SR corresponding to B and R, respectively.

[0035] The mapping of the spectral sensitivities S1, S2, S3, S4 of the diagram D1 onto the RGB target model defined by the model sensitivities SB, SG, SR of the diagram D4 is performed by weighting factors as shown by block B5 and matrix M in FIG. 2. In particular, the matrix M includes three columns corresponding to the three output color channels R, G, B of the target system and four rows corresponding to the four spectral sensitivities S1, S2, S3, S4 of the image sensors 108-114. Thus, each column R, G and B of the matrix M includes four values ​​corresponding to the four spectral sensitivities S1, S2, S3 and S4, respectively. Each of these values ​​specifies a weighting factor that determines the proportion in which the respective spectral sensitivities S1, S2, S3, S4 are mapped onto the respective color channel R, G and B. For example, the first row of the matrix M indicates that the first spectral sensitivity S1 is mapped onto the color channel R with a weighting factor of 0.092, onto the color channel G with a weighting factor of 0.28, and onto the color channel B with a weighting factor of 0.628. Similarly, according to the second row of the matrix M, the spectral sensitivity S2 is mapped onto the color channel R with a weighting factor of 0.08, onto the color channel G with a weighting factor of 0.525, and onto the color channel B with a weighting factor of 0.395. According to the third row of the matrix M, the spectral sensitivity S3 is mapped onto the color channel R with a weighting factor of 0.667, onto the color channel G with a weighting factor of 0.062, and onto the color channel B with a weighting factor of 0.271. According to the fourth row of the matrix M, the spectral sensitivity S4 is mapped onto the color channel R with a weighting factor of 0.605, onto the color channel G with a weighting factor of 0.053, and onto the color channel B with a weighting factor of 0.342.

[0036] In this regard, it is noted that the symbol "*" between blocks B3 and B4 in Fig. 2 indicates a mapping function that is applied to convert the four input channels represented by the spectral sensitivities S1, S2, S3 and S4 into three output channels represented by the color channels R, G and B. Since both the input channels S1, S2, S3, S4 and the output channels R, G, B are known in advance, it is possible to determine an appropriate mapping function for implementing the mapping from the input channels to the output channels.

[0037] Subsequently, as shown by block B6, the matrix M obtained in block B5 is multiplied by a vector V including the correction values ​​0.25, 0.5, and 0.25 determined according to the Bayer filter, so that a corrected model matrix Mc including corrected weighting coefficients is obtained, as shown by block B7, and this model matrix Mc can be stored in the memory 120.

[0038] Based on the model matrix Mc including the corrected weighting coefficients, color imaging can be performed in real time. For this purpose, grayscale images I1-I4 are acquired by the image sensors 108-114, as indicated by block B8. Then, the model matrix Mc is retrieved from the memory 120, and the processor 116 distributes the grayscale information contained in each grayscale image I1-I4 onto the color channels R, G, and B according to the weighting coefficients of the model matrix Mc, as indicated by block B9.

[0039] Specifically, according to the first row of Mc, the grayscale information of the first grayscale image I1 assigned to the spectral sensitivity S1 is mapped onto the color channel R with a weighting factor of 0.072, onto the color channel G with a weighting factor of 0.438, and onto the color channel B with a weighting factor of 0.491. Similarly, according to the second row of Mc, the grayscale information of the second grayscale image I2 assigned to the spectral sensitivity S2 is mapped onto the color channel R with a weighting factor of 0.053, onto the color channel G with a weighting factor of 0.689, and onto the color channel B with a weighting factor of 0.259. According to the third row of Mc, the grayscale information of the third grayscale image I3 assigned to the spectral sensitivity S3 is mapped onto the color channel R with a weighting factor of 0.628, onto the color channel G with a weighting factor of 0.116, and onto the color channel B with a weighting factor of 0.256. According to the fourth row of Mc, the grayscale information of the fourth grayscale image I4 assigned to the spectral sensitivity S4 is mapped onto the color channel R with a weighting factor of 0.575, onto the color channel G with a weighting factor of 0.100, and onto the color channel B with a weighting factor of 0.325. As a result, image values ​​in the color channels R, G, and B are obtained based on the grayscale information contained in the grayscale images I1-I4.

[0040] Next, as indicated by block B10, the processor 116 may determine a maximum value from all image values ​​acquired in block B9. Furthermore, the processor 116 may determine a scale factor and normalize the image values ​​by the scale factor, as indicated by block B11. Then, the processor 116 may perform white balance processing, as indicated by block B12. Finally, the resulting digital RGB color image is displayed on the display device 118.

[0041] It should be noted that the model matrix Mc including the weighting coefficients Mc can be determined prior to real-time color imaging based on the acquisition of the grayscale images I1-I4. In particular, since the matrix Mc can be calculated independently of the grayscale images I1-I4, the matrix Mc can be determined only once and stored for subsequent imaging.

[0042] Furthermore, it should be noted that the process described above includes optional steps that may be omitted. For example, normalization of the spectral sensitivities S1-S4 based on the quantum yield Y as indicated by block B2 may not be necessary depending on the particular image sensor used. Similarly, the Bayer matrix correction may be omitted if a different model color space is used.

[0043] As used in this specification, the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as " / ".

[0044] Although some aspects have been described in the context of an apparatus, it will be apparent that these aspects also represent a description of a corresponding method, where a block or apparatus corresponds to a step or feature of a step, and similarly, aspects described in the context of a step also represent a description of a corresponding block or item or feature of a corresponding apparatus.

[0045] Some embodiments relate to a microscope including a system as described in relation to one or more of the figures of Figures 1 to 2. Alternatively, the microscope may be part of a system as described in relation to one or more of the figures of Figures 1 to 2 or may be connected to a system as described in relation to one or more of the figures of Figures 1 to 2. Figure 3 shows a schematic diagram of a system 300 configured to perform the methods described herein. The system 300 includes a microscope 310 and a computer system 320. The microscope 310 is configured to capture images and is connected to the computer system 320. The computer system 320 is configured to perform at least some of the methods described herein. The computer system 320 may be configured to execute machine learning algorithms. The computer system 320 and the microscope 310 may be separate entities, but may be integrated in one common housing. The computer system 320 may be part of a central processing system of the microscope 310 and / or the computer system 320 may be part of a subordinate part of the microscope 310, such as a sensor, actor, camera or lighting unit of the microscope 310.

[0046] The computer system 320 may be a local computing device (e.g., a personal computer, laptop, tablet computer, or mobile phone) with one or more processors and one or more storage devices, or may be a distributed computing system (e.g., a cloud computing system with one or more processors and one or more storage devices distributed at various locations, such as local clients and / or one or more remote server farms and / or data centers). The computer system 320 may include any circuit or combination of circuits. In one embodiment, the computer system 320 may include one or more processors, which may be of any type. As used herein, a processor may contemplate any type of computing circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA), or any other type of processor or processing circuit, for example, of a microscope or a microscope component (e.g., a camera). Other types of circuits that may be included in computer system 320 may be custom circuits, application specific integrated circuits (ASICs), such as one or more circuits (such as communications circuits) used in wireless devices such as cell phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. Computer system 320 may also include one or more storage devices, which may include one or more memory elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard drives and / or one or more drives handling removable media, such as compact discs (CDs), flash memory cards, digital video discs (DVDs), and the like.Computer system 320 may also include a display device, one or more speakers and a keyboard and / or controller which may include a mouse, a trackball, a touch screen, a voice recognition device, or any other device that enables a user of the system to input information to and receive information from computer system 320.

[0047] Some or all of the steps may be performed by (or using) a hardware apparatus, such as, for example, a processor, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, any one or more of the crucial steps may be performed by such an apparatus.

[0048] Depending on certain implementation requirements, the embodiments of the present invention can be implemented in hardware or software. The implementation can be performed by a non-transitory recording medium, such as a digital recording medium, for example a floppy disk, a DVD, a Blu-ray, a CD, a ROM, a PROM and EPROM, an EEPROM or a FLASH memory, on which electronically readable control signals are stored, which cooperate (or can cooperate) with a programmable computer system to implement the respective methods. Thus, the digital recording medium can be computer readable.

[0049] Some embodiments of the present invention include a data carrier having electronically readable control signals capable of cooperating with a programmable computer system to perform any of the methods described herein.

[0050] Generally, embodiments of the present invention can be implemented as a computer program product comprising program code which is operable to perform any of the methods when the computer program product is run on a computer, the program code may for example be stored on a machine readable carrier.

[0051] Another embodiment comprises the computer program for performing any of the methods described herein, stored on a machine readable carrier.

[0052] In other words, an embodiment of the present invention is, therefore, a computer program having a program code for performing any of the methods described herein, when the computer program runs on a computer.

[0053] Therefore, another embodiment of the present invention is a recording medium (or data carrier or computer readable medium) containing a computer program stored thereon for performing any of the methods described herein when executed by a processor. The data carrier, digital recording medium or recording medium is typically tangible and / or non-transitory. Another embodiment of the present invention is an apparatus as described herein, including a processor and a recording medium.

[0054] A further embodiment of the invention is therefore also a data stream or a sequence of signals representing the computer program for performing any of the methods described herein, the data stream or the sequence of signals being for example adapted to be transmitted via a data communication connection, for example the Internet.

[0055] Another embodiment comprises a processing means, for example a computer, or a programmable logic device configured to or adapted to perform any of the methods described herein.

[0056] Another embodiment comprises a computer having the computer program installed thereon for performing any of the methods described herein.

[0057] Another embodiment of the invention includes an apparatus or system configured to transfer (e.g., electronically or optically) a computer program for implementing any of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may, for example, include a file server to transfer the computer program to the receiver.

[0058] In some embodiments, a programmable logic device (e.g., a field programmable gate array) may be used to perform some or all of the functionality of the methods described herein. In some embodiments, a field programmable gate array may cooperate with a microprocessor to perform any of the methods described herein. In general, the methods are advantageously performed by any hardware apparatus. [Explanation of symbols]

[0059] List of References 100 Fluorescence Microscope 102 Imaging Unit 104 Object 106 Digital Imaging Device 108~114 Image sensor 116 processors 118 Display device 120 Memory 300 Systems 310 Microscope 320 Computer Systems S1~S4 Spectral Sensitivity S1a~S4a Corrected Spectral Sensitivity Y Sensor quantum yield SB, SG, SR model sensitivity B1~B12 Block D1~D4 diagram M,Mc matrix V Vector

Claims

1. A microscope (100) including a digital imaging device (106), The digital imaging device (106) includes a processor (116); the processor (116) is configured to acquire a plurality of digital grayscale images (I1-I4) of the object (104) at a plurality of respective different spectral sensitivities (S1-S4), each digital grayscale image (I1-I4) including grayscale information based on a respective one of the respective different spectral sensitivities (S1-S4); the processor (116) is configured to obtain a number of weighting factors (Mc) for each digital grayscale image (I1-I4), the weighting factors being assigned to a number of color channels (R, G, B) defining a predetermined color space; said processor (116) is configured to synthesize a digital color image of said object (104) from said digital grayscale images (I1-I4) into said color space by distributing said grayscale information of each grayscale image (I1-I4) onto said color channels (R, G, B) according to said weighting coefficients (Mc); Microscope (100).

2. the number of the spectral sensitivities (S1 to S4) of the grayscale images (I1 to I4) is different from the number of the color channels (R, G, B) of the color space; The microscope (100) of claim 1.

3. The weighting coefficients are represented by a matrix (Mc), The number of rows of the matrix (Mc) is equal to the number of the digital grayscale images (I1 to I4); The number of columns of the matrix (Mc) is equal to the number of color channels (R, G, B) of the color space. A microscope (100) according to claim 1 or 2.

4. The grayscale information of each digital grayscale image (I1-I4) is corrected for the quantum yield (Y) of the sensor. A microscope (100) according to any one of claims 1 to 3.

5. The weighting coefficients (M) are corrected according to a Bayer filter. A microscope (100) according to any one of the preceding claims.

6. the processor (116) is configured to determine a maximum value from all pixel values ​​constituting the digital color image and normalize the pixel values ​​based on the maximum value. A microscope (100) according to any one of the preceding claims.

7. the processor (116) is configured to perform white balancing on the digital color image; A microscope (100) according to any one of the preceding claims.

8. The microscope (100) includes a plurality of image sensors (108-114); the plurality of image sensors (108-114) are configured to acquire the plurality of digital grayscale images (I1-I4) at the plurality of different respective spectral sensitivities (S1-S4); A microscope (100) according to any one of the preceding claims.

9. Each of the image sensors (108-114) is formed by a wide field of view sensor. The microscope (100) of claim 8.

10. The microscope (100) includes a display device (118) configured to display the digital color image in real time. A microscope (100) according to any one of the preceding claims.

11. The color space is an RGB color space. A microscope (100) according to any one of the preceding claims.

12. 1. A method for generating a digital color image of an object (104), comprising: The method includes the step of acquiring, using a microscope, a plurality of digital grayscale images (I1-I4) of the object (104) at a plurality of respective different spectral sensitivities (S1-S4), each digital grayscale image (I1-I4) including grayscale information based on a respective one of the respective different spectral sensitivities (S1-S4); The method includes the step of obtaining a number of weighting factors (Mc) for each digital grayscale image, the weighting factors (Mc) being assigned to a number of color channels (R, G, B) defining a predetermined color space; The method includes the step of synthesizing a digital color image of the object from the digital grayscale images (I1-I4) into the color space by distributing the grayscale information of each grayscale image (I1-I4) onto the color channels (R, G, B) according to the weighting coefficients (Mc); method.

13. The weighting coefficient (Mc) is obtained by reading the weighting coefficient (Mc) from a memory (120).

13. The method of claim 12.

14. 14. A computer program having a program code for performing the method according to claim 12 or 13, when the computer program is run on a processor (116).

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