an embodiment of the present invention will be described . this embodiment is an embodiment of a spectral imaging fluorescent confocal laser microscope system . fig1 is a configuration diagram of this system . as shown in fig1 , this system includes a main body of a microscope 10 , a computer 20 connected thereto , and an input device 30 and a displaying device 40 connected thereto . the input device 30 is a mouse , a keyboard , and so on , and the displaying device 40 is an lcd or the like . in the main body 10 , a laser light source 11 , a dichroic mirror 12 , an optical scanner 13 , an objective lens 14 , a sample 15 , an observation lens 16 , a pinhole mask 17 , a spectroscopic element 18 , and a multichannel light detector 19 are placed . the sample 15 is labeled by plural types ( for example , three types ) of fluorescent reagents , and the multichannel light detector 19 has many ( for example , 32 ) wavelength channels . the computer 20 includes a cpu 23 , a rom 24 into which a basic operation program of the cpu 23 is written , a ram 25 used as a temporary storage means while the cpu 23 is operating , a hard disk drive 26 to save information for a long time . an interface circuit 27 interfacing the input device 30 and the displaying device 40 , a / d converting circuits 21 1 , 21 2 , . . . , 21 32 of the same number as wavelength channels of the multichannel light detector 19 , and frame memories 22 1 22 2 . . . , 22 32 of the same number as the a / d converting circuits . the frame memories 22 1 , 22 2 , . . . , 22 32 , the hard disk drive 26 , the cpu 23 , the rom 24 , the ram 25 , the interface circuit 27 are connected via a bus 20 b . an operation program of the cpu 23 necessary for this system is previously stored in the hard disk drive 26 . laser light ( for example , having a wavelength of 488 nm ) is emitted from the laser light source 11 of the main body of the microscope 10 . this laser light is reflected by the dichroic mirror 12 and collected at a point on the sample 15 via the optical scanner 13 and the objective lens 14 in order . at the light collecting point , fluorescence ( for example , having a wavelength of 510 nm to 550 nm ) is generated , and when entering the dichroic mirror 12 via the objective lens 14 and the optical scanner 13 in order , the fluorescence is transmitted through this dichroic mirror 12 and enters the pinhole mask 17 via the observation lens 16 . this pinhole mask 17 forms a conjugate relation with the sample 15 by the observation lens 16 and the objective lens 14 and has a function of letting only a necessary ray of light of the fluorescence generated on the sample 15 pass therethrough . as a result , a confocal effect of the main body of the microscope 10 can be obtained . when entering the spectroscopic element 8 , the fluorescence which has passed through the pinhole mask 17 is separated into plural wavelength components . these respective wavelength components enter wavelength channels different from each other of the multichannel light detector 19 and detected independently and simultaneously . the respective wavelength channels ( here , 32 wavelength channels ) of the multichannel light detector 19 detect , for example , 32 kinds of wavelength components different in steps of 5 nm in a wavelength range from 510 nm to 550 nm . respective signals outputted from the 32 wavelength channels are imported in parallel into the computer 20 and individually inputted to the frame memories 22 1 , 22 2 , . . . , 22 32 via the a / d converting circuits 21 1 , 21 2 , . . . , 21 32 . this multichannel light detector 19 and the optical scanner 13 are synchronously driven , and thereby the signals are repeatedly outputted from the multichannel light detector 19 during a period of two - dimensional scanning at the light collecting point on the sample 15 . at this time , images of the respective wavelength channels of the sample 15 are gradually accumulated in the frame memories 22 1 , 22 2 , . . . , 22 32 . the images ( channels images d 1 , d 2 , . . . , d 32d ) of the respective wavelength channels accumulated in the frame memories 22 1 , 22 2 , . . . , 22 32 are read in an appropriate timing by the cpu 23 , integrated into one spectral image f , and then stored in the hard disk drive 26 . incidentally , in the hard disk drive 26 of the computer 20 , in addition to this spectral image f , emission spectral data of the fluorescent reagents used for the sample 15 is previously stored . this emission spectral data is disclosed by manufactures of the fluorescent reagents or the like and loaded into the computer 20 , for example , by the internet , a storage medium , or the like . next , the operation of the cpu 23 after the spectral image f is acquired will be described . fig2 is an operational flowchart of the cpu 23 . as shown in fig2 , after executing noise reducing processing constituted by normalizing processing ( step s 1 ), smoothing processing ( step s 2 ), and denormalizing processing ( step s 3 ), the cpu 23 executes unmixing processing ( step s 4 ), and displaying processing ( step s 5 ). these steps will be described below step by step . in this step , first , as shown in fig3 ( a ) , the cpu 23 refers to spectral curves of respective pixels from the spectral image f . in fig3 ( a ) , only spectral curves of some four pixels ( a first pixel , second pixel , third pixel , fourth pixel ) are shown . the horizontal axis of the spectral curve is a wavelength channel , and the vertical axis thereof is a brightness value . brightness levels of the spectral curves of the respective pixels vary as shown in fig3 ( a ) . a brightness integral value a 1 of the spectral curve of the first pixel indicates a total brightness of the first pixel , a brightness integral value a 2 of the spectral curve of the second pixel indicates a total brightness of the second pixel , a brightness integral value a 3 of the spectral curve of the third pixel indicates a total brightness of the third pixel , and a brightness integral value a 4 of the spectral curve of the fourth pixel indicates a total brightness of the fourth pixel . further , as shown in fig3 ( a ) , shapes of the spectral curves vary among the respective pixels . between close pixels , there is a high possibility that rough shapes of the spectral curves are similar , but fine shapes of the spectral curves differ from each other even if the pixels are close since random measurement noise is superimposed . then , as shown in fig3 ( b ) , the cpu 23 normalizes the spectral curves of the respective pixels such that their brightness integral values a become one . in the normalization of each spectral curve , it is only required to multiply brightness values of the respective wavelength channels of the spectral curve by a normalizing coefficient =( 1 / current brightness integral value ). when a spectral image f ′ constituted by the normalized spectral curves is referred to here as shown at the right side of fig3 , any of the total brightnesses of the respective pixels becomes one in the spectral image f ′. that is to say , brightness information of the spectral curves of the respective pixels is excluded from the spectral image f ′, and only shape information of the spectral curves of the respective pixels is maintained . hereinafter , respective wavelength components ( channel images ) of this spectral image f ′ are represented as d 1 ′, d 2 ′, . . . , d 32 ′. in this step , as shown in fig4 ( a ) , the cpu 23 performs averaging filter processing on each of the above channel images d 1 ′, d 2 ′, . . . , d 32 ′. therefore , each of the channel images d 1 ′, d 2 ′, . . . , d 32 ′ is smoothed in a spatial direction . in the averaging filter processing for the channel image d ′, a mask ( which is a computational mask ), for example , having an opening of three pixels high by three pixels wide is used . this mask is put into the channel image d ′, and the brightness value of a target pixel located at the center of the opening of the mask is replaced with a brightness mean value of all the pixels in the opening . by repeatedly performing this processing while shifting a mask position on the channel image d ′, processing of the whole area of the image is completed . here , if the respective channel images after the smoothing are represented as d 1 ″, d 2 ″, . . . , d 32 ″ as shown in the lower left of fig4 and a spectral image f ″ constituted by these channel images d 1 ″, d 2 ″, . . . , d 32 ″ is referred to , in the spectral image f ″, as shown in fig4 ( b ) , shapes of the spectral curves of the respective pixels become smooth . this is because the shapes of the spectral curves of the respective pixels are influenced by the shapes of the spectral curves of their adjacent pixels by the smoothing . this indicates that noise is removed from the shape information of the spectral curves of the respective pixels . in this step , as shown in fig4 ( c ) , the cpu 23 denormalizes the spectral curves of the respective pixels constituting the spectral image f ″ such that their brightness integral values return to the brightness integral values before the normalization ( see fig3 ( a ) ). concerning the spectral curve of the first pixel , it is denormalized such that its brightness integral value returns to the value a 1 before the normalization , concerning the spectral curve of the second pixel , it is denormalized such that its brightness integral value returns to the value a 2 before the normalization , concerning the spectral curve of the third pixel , it is denormalized such that its brightness integral value returns to the value a 3 before the normalization , and concerning the spectral curve of the fourth pixel , it is denormalized such that its brightness integral value returns to the value a 4 before the normalization . in the denormalization of each spectral curve , it is only required to multiply brightness values of the respective wavelength channels of the spectral curve by an denormalizing coefficient =( brightness integral value before normalization / current brightness integral value ). a spectral image constituted by the above spectral curves after the denormalization is stored again as the spectral image f in the hard disk drive 26 as shown in the lower right of fig4 . in this spectral image f , the brightness information of the spectral curves of the respective pixels is recovered by the denormalization . besides , noise is removed from the shape information of the spectral curves of the respective pixels as described above . accordingly , this spectral image f accurately represents the state of the sample 15 . in this step , first , the cpu 23 reads the spectral image f and the emission spectral data of the fluorescent reagents from the hard disk drive 26 . as shown in fig5 ( a ) , ( b ), ( c ), the emission spectral data represents emission spectral curves s 1 , s 2 , s 3 of the three types of fluorescent reagents ( a first reagent , second reagent , third reagent ). these emission spectral curves s 1 , s 2 , s 3 are each represented by a one - dimensional matrix such as shown in equation ( 1 ). note that an element s ij in equation ( 1 ) is a brightness value of an ith wavelength of a jth reagent . the number of elements in a wavelength direction of this matrix is set to 32 to match the data amount in a wavelength direction of the spectral image f (= the number of wavelength channels of the multichannel light detector 19 ). the cpu 23 performs unmixing processing of the spectral image f based on these emission spectral curves s 1 , s 2 , s 3 , and the unmixing is performed for each pixel of the spectral image f . a spectral curve f of some pixel included in the spectral image f is represented by a one - dimensional matrix such as shown in equation ( 2 ). an element f l is a brightness value of an ith wavelength channel of this pixel . accordingly , if the contribution ratio of the first reagent to this pixel is taken as p 1 , the contribution ratio of the second reagent thereto is taken as p 2 , and the contribution ratio of the third reagent thereto is taken as p 3 , the spectral curve f of this pixel is represented by equation ( 3 ). further , if the respective emission spectral curves of the three types of fluorescent reagents are brought together and represented by one matrix s as shown in equation ( 4 ), and the respective contribution ratios of the three types of fluorescent reagents are brought together and represented by one matrix p as shown in equation ( 5 ), equation ( 3 ) is transformed as shown in equation ( 6 ). [ equation ⁢ ⁢ 5 ] p = [ p 1 p 2 p 3 ] ( 5 ) [ equation 6 ] f = s · p ( 6 ) hence , the cpu 23 can unmix this pixel by assigning information on the spectral curve f of this pixel and information on the emission spectral curve s to equation ( 6 ) and solving this equation for the contribution ratio p . note , however , that since the number of wavelength channels ( here , 32 ) is set larger than the number of types of fluorescent reagents ( here , 3 ) as described above in this system , the cpu 23 applies a least squares method . the least squares method is to prepare equation ( 7 ) with consideration given to an error a in equation ( 6 ) and find the contribution ratio p such that a square value of the error a becomes minimum . an equation to calculate the contribution ratio p by this least squares method is shown as in equation ( 8 ). note that s t is a transposed matrix of s . accordingly , the cpu unmixes this pixel by assigning the information on the spectral curve f of this pixel and the information on the emission spectral curve s to this equation ( 8 ). then , the cpu 23 performs this unmixing on all the pixels of the spectral image f , respectively , and completes this step . as just described , the unmixing processing in this step is performed by the well - known least squares method , but since the spectral image f accurately represents the state of the sample 15 as described above , the accuracy of this unmixing processing is higher than that of the conventional one . in this step , the cpu 23 displays the information on the contribution ratios ( contribution ratios of the respective fluorescent reagents to the respective pixels ) found by the unmixing processing on the displaying device 40 . the information on the contribution ratios may be displayed as numeric data , but in order to intuitively inform a user of it , it is desirable that the cpu 23 creates an unmixed image colored according to the contribution ratios and displays it . as described above , the computer 20 of this system removes noise from the spectral image prior to the unmixing processing , but this noise reducing processing does no damage to the brightness information of the spectral curves of the respective pixels as described above , so that the spectral image f which accurately represents the state of the sample 15 can be obtained . hence , the accuracy of the unmixing processing by the computer 20 , that is , the performance of this system is certainly improved . incidentally , in the noise reducing processing ( steps s 1 to s 3 ) of this system , the standards of the normalization and the denormalization of the spectral curve are set to the brightness integral value of the spectral curve , but may be set to a brightness maximum value or a brightness intermediate value instead of the brightness integral value . in fig6 and fig7 , changes of the spectral curves when the standards of the normalization and the denormalization are set to the brightness maximum value are shown . referring to fig6 and fig7 , it can be seen that peaks of the spectral curves of the respective pixels before the normalization are i 1 , i 2 , i 3 , i 4 , but all become one after the normalization , and after the denormalization , return to the values i 1 , i 2 , i 3 , i 4 before the normalization . further , in the smoothing processing ( step s 2 ) of this system , the averaging filter processing is applied , but instead of the averaging filter processing , a different spatial filter processing such as weighted averaging filter processing or a median - filter processing may be applied . for reference &# 39 ; s sake , the median - filter processing is to find a brightness intermediate value of all the pixels in the opening instead of calculating the brightness mean value thereof . it is desirable that the type of such filter processing be selected appropriately according to the type of the measurement noise generated in the main body of the microscope 10 . for reference &# 39 ; s sake , the averaging filter processing is effective when nose is generated uniformly on the channel image , and the median - filter processing is effective when noise is generated suddenly on the channel image ( salt - and - pepper noise ). furthermore , in the smoothing processing ( step s 2 ) of this system , the size of the mask ( size of a filter ) is 3 pixels × 3 pixels = 9 pixels , but may be changed to a different size . it is desirable that this size be selected appropriately according to the type of the measurement noise generated in the main body of the microscope 10 . moreover , in the noise reducing processing ( steps s 1 to s 3 ) of this system , the start timing of the smoothing processing is after the normalization of the spectral curves of all the pixels , but it is also possible to normalize spectral curves of required pixels on a case - by - case basis while performing the smoothing processing . further , in this system , the operation program of the cpu 23 is previously stored in the hard disk drive 26 , but part or all of the program may be installed into the computer 20 from outside via the internet , a storage medium , or the like . furthermore , in this system , each processing is executed by the computer 20 , but part or all of the operations of the computer 20 may be executed by a device ( control / image processing device ) dedicated to the main body of the microscope 10 . moreover , the main body of the microscope 10 of this system uses the multichannel light detector 19 to detect respective wavelength components of incident light , but instead of the multichannel light detector 19 , a combination of one - channel light detector and a movable mask , a combination of plural one - channel light detectors and plural filters , or the like may be used . note , however , that the use of the multichannel light detector 19 is advantageous in that space can be saved . further , the main body of the microscope 10 of this system is a fluorescent microscope which detects fluorescence generated on the sample 15 , but may be a microscope which detects transmitted light or reflected light of light illuminating the sample 15 . in this case , instead of the dichroic mirror 12 , a beam splitter is used . furthermore , the main body of the microscope 10 of this system is a confocal microscope which confocally detects light from the sample 15 , but the function of this confocal detection may be omitted . in this case , the pinhole mask 17 becomes unnecessary . additionally , the main body of the microscope 10 of this system is a scanning microscope which optically scans the sample 15 , but may be a non - scanning microscope . in this case , the optical scanner 13 becomes unnecessary . namely , the present invention can be applied to various devices which perform spectral imaging . the many features and advantages of the invention are apparent from the foregoing description . it is to be understood that the invention is not limited to the described embodiments , which are intended to be illustrative and not limiting . as will readily occur to those skilled in the art , numerous changes and modifications are possible in keeping with the principles and spirit of the invention , the scope of which is defined in the appended claims .