Abstract:
Surface defects in a reflection scan of a print made with visible light are corrected by using a scan of the print made with infrared light. In accordance with the present invention, surface defects in a reflection scan of an image consisting of pixels made with visible light are corrected by using a scan of the image consisting of pixels made with infrared light. This correction of surface defects is preformed by first establishing for each pixel an upper and lower bound for defect intensity based on the infrared record. The corresponding visible pixel is then corrected by subtracting the combination of upper and lower bound resulting in a corrected pixel.

Description:
RELATED APPLICATION 
     This application relies on U.S. Provisional Application Serial No. 60/077,903 filed Mar. 13, 1998, and entitled “Image Defect Correction in Transform Space.” 
    
    
     TECHNICAL FIELD OF INVENTION 
     This invention relates to electronic scanning of images, and more particularly to the scanning of photographic prints by reflected light and the removal of surface defects. 
     BACKGROUND OF THE INVENTION 
     The present invention is an improvement on a method of correcting defects in a film image using infrared light as taught in U.S. Pat. No. 5,266,805 issued to Albert Edgar, the present inventor. The underlying physics enabling this method is illustrated in FIG.  1 . In FIG. 1 it is noted that with any color of visible light, such as green light, one or more dyes in a color film absorb light with corresponding low transmission of the light; however, in the infrared wavelength range, the common image forming dyes have a very high transmission approaching 100%, and therefore have little or no effect on transmitted infrared light. On the other hand, most surface defects, such as scratches, fingerprints, or dust particles, degrade the image by refracting light from the optical path. This refraction induced transmission loss is nearly the same in the infrared as it is in the visible, as illustrated in FIG.  1 . 
     Continuing now with FIG. 2, a film substrate  201  has embedded in it a dye layer  202 . Infrared light  204  (FIG. 2 a ) impinging on the film  201  will pass through the film and emerge as light  206  with nearly 100% transmission because the dye  202  does not absorb infrared light. Conversely, visible light  208  (FIG. 2 b ) will be absorbed by the dye  202 . If the dye density is selected for a 25% transmission, then 25% of the visible light  210  will be transmitted by the film  201 . 
     Now assume the film is scratched with a notch  214  (FIG. 2 c ) such that 20% of the light will be refracted from the optical path before penetrating into the film  201 . When a beam of infrared light  216  strikes the film  201 , 20% will be diverted due to the notch  214 , and a beam of 80% of the infrared light  218  will be transmitted. Finally, let a beam of visible light  220  (FIG. 2 d ) impinge on the film  201 . Again 20% of the light  222  is diverted by the notch  214 , leaving 80% of the visible light to penetrate the film  201 . However, the dye layer  202  absorbs 75% of that 80%, leaving only 25% of 80%, or 20% of the original light  224 , to pass through the film  201 . 
     In general, the beam left undiverted by the defect is further divided by dye absorption. In visible light, that absorption represents the desired image, but in infrared that dye absorption is virtually zero. Thus, by dividing the visible light actually transmitted for each pixel by the infrared light actually transmitted, the effect of the defect is divided out, just like division by a norming control experiment, and the defect is thereby corrected. This division process is further clarified in FIG.  3 . The value of a pixel  302  of a visible light image  304  is divided with operator  306  by the value of the corresponding pixel  308  of the infrared light image  310 . The resultant value is placed into pixel  312  of the corrected image  314 . Typically, the process is repeated with visible image  304  received under blue light, then green light, then red light to generate three corrected images representing the blue, green, and red channels of the image  304 . 
     FIG. 4 is similar to FIG. 3 in that it shows a process for removing the effect of defects from a visible light image  404  using an infrared light image  406 . Although the operator  408  in FIG. 4 is a subtraction, FIG. 4 is mathematically identical to FIG. 3 because the same result is obtained either by dividing two numbers, or by taking the logarithm of each, subtracting the two values in the logarithmic space, then taking the inverse logarithm of the result. However, the arrangement of FIG. 4 enables many additional useful functions because within the dotted line  402 , the signals from images  404  and  406  may be split and recombined with a variety of linear functions that would not be possible with the nonlinear processing using the division operator of FIG.  3 . 
     For example, in FIG. 5 a visible image  502  and an infrared image  504  are processed by logarithmic function blocks  506  and  508 , respectively, to enter the linear processing dotted block  510  equivalent to block  402  of FIG.  4 . After processing within block  510  is completed, the antilog is taken at function block  512  to produce the corrected image  514 . 
     Internal to linear processing block  510 , the logarithmic versions of the visible and infrared images are divided into high pass and low pass images with function blocks  520 ,  522 ,  524 , and  526 . These function blocks are selected such that when the output of the high and low pass blocks are added, the original input results. Further, the high pass function blocks  522  and  526  are equal, and the low pass function blocks  520  and  524  are equal. Under these assumptions, and under the further temporary assumption that the gain block  530  is unity, the topology in linear block  510  produces a result identical to the single subtraction element  408  for FIG.  4 . 
     Without the logarithmic function blocks  506 ,  508 , and  512 , the split frequency topology shown in block  510  would not work. The output of a high pass filter, such as blocks  522  and  526 , averages zero because any sustained bias away from zero is a low frequency that is filtered out in a high frequency block. A signal that averages to zero in small regions obviously passes through zero within those small regions. If function block  540  were a division, as would be required without the logarithmic operators, then the high pass visible signal  542  would often be divided by the zero values as the high pass infrared signal  544  passed through zero, resulting in an infinite high pass corrected signal  546 , which obviously would give erroneous results. However, as configured with block  540  as a subtraction, the process is seen to avoid this problem. 
     The split frequency topology of FIG. 5 appears to be a complicated way to produce a mathematically equal result to that produced by the simple topology of FIG.  3  and FIG.  4 . However, by separating the high frequencies as shown in FIG. 5, it is possible to overcome limitations in the scanner system by now allowing the gain block  530  to vary from unity. A typical scanner will resolve less detail in infrared light than in visible light. By letting gain block  530  have a value greater than unity, this deficiency can be controlled and corrected. 
     Often, however, the smudging of detail by a scanner in the infrared region relative to the visible region will vary across the image with focus shifts or the nature of each defect. By allowing the gain block  530  to vary with each section of the image, a much better correction is obtained. In particular, the value of gain is selected such that after subtraction with function block  540 , the resulting high frequency signal  546  is as uncorrelated to the high frequency defect signal  548  as possible. If given the task, a human operator would subtract more or less of the defect signal  548  as controlled by turning the “knob” of gain block  530 . The human operator would stop when the defect “disappears” from corrected image signal  546  as seen by viewing the corrected image  514 . This point is noted by the human operator as “disappearance” of the defect and is mathematically defined as the point at which the defect signal  544  or  548  and the corrected signal  546  are uncorrelated. This process could be repeated for each segment of the image with slightly different values of gain resulting as the optimum gain for each segment. 
     Despite the flexibility introduced by the gain block  530  of FIG. 5, it has been found that often a defect is incompletely nulled because deficiencies in the scanner cause the defect to look different in the infrared and the visible, such that no setting of gain can eliminate all aspects of the defect. 
     A need has thus arisen for an improved method for image defect correction. 
     SUMMARY OF THE INVENTION 
     In accordance with the present invention, surface defects in a reflection scan of an image consisting of pixels made with visible light are corrected by using a scan of the image consisting of pixels made with infrared light. This correction of surface defects is performed by first establishing for each pixel an upper and lower bound for defect intensity based on the infrared record. The corresponding visible pixel is then corrected by substracting the combination of upper and lower bound resulting in a corrected pixel. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     For a more complete understanding of the present invention and for further advantages thereof, reference is now made to the following Description of the Preferred Embodiments taken in conjunction with the accompanying Drawings in which: 
     FIG. 1 compares light transmission of dyes with light transmission of a surface defect; 
     FIGS. 2 a-d  compare visible and infrared transmissions of a film with and without a defect; 
     FIG. 3 illustrates an overview of a prior art process for infrared surface defect correction; 
     FIG. 4 illustrates a method of surface defect correction applied in logarithmic space; 
     FIG. 5 illustrates a method of surface defect correction applied in split frequency space; 
     FIG. 6 teaches the present method of bounded subtraction used in surface defect correction; 
     FIGS. 7 a - 7   f  graphically detail the effect of the bounded subtraction shown in FIG. 6; 
     FIG. 8 is a flow chart illustrating details of the present method for accomplishing bounded subtraction; 
     FIG. 9 a - 9   e  graphically show bounded subtraction applied in split frequency space; 
     FIG. 10 shows an effect of bounded subtraction in two dimensions; 
     FIG. 11 teaches defect correction applied in transform space; 
     FIG. 12 further details correction in transform space with displacement; 
     FIG. 13 is a flow chart illustrating the method for obtaining a correlation value; 
     FIGS. 14 a - 14   e  show graphically the calculation of upper and lower bounds; and 
     FIG. 15 is a flow chart illustrating the method for obtaining the upper and lower bounds. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The topology of FIG. 6 of the present invention seeks to overcome the problem of incompletely nulling a defect by utilizing a bounded subtraction function block  602  capable of totally zeroing a defect within a bounded range. 
     FIG. 6 assumes operation within the logarithmic domain as demarcated by the dotted box  402  of FIG. 4, and further assumes operation on images that have been band passed or high passed as shown previously in FIG. 5 such that the values of the pixels comprising the images average to zero within a region. Because the values of the pixels average to zero, zero is a “base” to which the image can be driven that will always give a reasonable erasure of detail. If the image were not band passed or high passed, setting pixels to zero would produce black dots that would not represent a reasonable erasure of detail. 
     Further, it should be understood that “zero” is a relative term, and that a fixed bias, or a bias varying with the low frequency of the image, could be introduced, and that setting pixels to “zero” would represent setting them to this bias value. In AC coupled analog electronics, “zero” may or may not represent zero absolute volts, and “zero” is used here in that sense. 
     Continuing with the description of the preferred embodiment shown in FIG. 6, a pixel  604  from infrared image  606  is processed in conjunction with adjacent pixels by an upper bound function block  608  to estimate, all things considered, what the maximum value for that pixel might be if scanned with an ideal scanner. That maximum value must account for errors in registration, sharpness, and so forth. That maximum value is placed in the upper bound infrared image  610  at pixel  612 . Similarly, the same original pixel  604  is processed with adjacent pixels by the lower bound function block  614  to produce a lower bound estimate placed in pixel  616  of the lower bound infrared image  618 . 
     The bounded subtractor function block  602  receives the value of the visible pixel  620  from visible image  622 . The upper bound estimate  612  is subtracted from this visible pixel to reduce an upper bound corrected estimate, and the lower bound estimate  616  is subtracted to reduce a lower bound corrected estimate. To the extent the estimators  608  and  614  are operating correctly, the ideal corrected value will lie between the upper and lower bound corrected estimates. An assumption used to select one of the corrected estimates is that if a mistake is made in choosing one estimate, the mistake will be less noticeable if it results in an estimated value closer to zero than if it results in an estimated value farther from zero. Therefore the one of the two upper and lower corrected estimates that is closest to zero is selected as the final estimate. If one estimate is positive and the other negative, and therefore zero is between the two estimates, then zero is output as the final estimate from the bounded subtraction block  602  to place in pixel  626  of the corrected image  628 . 
     Turning now to FIG. 7, the operation and effect of the bounded subtractor are further explained. In FIG. 7, a one-dimensional image is portrayed, which may be a single scan line through a two-dimensional image. It should be understood that the same concepts apply in one or two dimensions. 
     In FIG. 7 a , an infrared defect signal  702  is received from an imperfect scanner. An estimate is made from this defect signal  702  of the range of what might have been received from an ideal scanner. In FIG. 7 b , the signal may be higher  704  or lower  706  in magnitude, or may have been further left  708  or right  710 . With all this considered, an upper bound  716  (FIG. 7 c ) and lower bound  718  are found as the limits of the curves  704  to  710 . 
     A perfect visible image signal  720  (FIG. 7 d ) is contained in the film. Because the film also has a surface defect, the scanned signal  720  received from the scanner approximates the sum of the visible image  720  and the defect signal  702 , shown as signal  722  in FIG. 7 e . Also copied are the upper and lower bounds of the defect  716  and  718 . At position  730 , the received visible image  722  is above both the upper and lower bounds  716  and  718 , so the greater of the two, the upper bound, is subtracted. At position  732 , the received visible image  722  is between the upper and lower bounds, and so the corrected signal is set to zero. The corrected signal  734  (FIG. 7 f ) is seen to contain the original features of the perfect image  720  inside the film. The bounded subtractor method has, however, reduced the intensity of the details on the assumption that the defect signal is only known within bounds, and it is better to err on the side of a smaller signal than a larger, more noticeable one. 
     The bounded subtractor is further described in FIG.  8 . In this algorithm, two prototype corrections C 1  and C 2  are attempted using the upper and lower bounds U and L. If the two prototype corrections C 1  and C 2  are on opposite sides of zero, which may be tested by asking if their product is negative, then the final correction C is set to zero. If C 1  and C 2  are on the same side of zero, then both have the same sign. If both are positive, the prototype correction using the upper, biggest bound is used to set the final correction, and otherwise if both are negative, then the prototype correction using the lower, most negative bound will be closer to zero, and is used to set the final correction for the pixel under computation. 
     At step  800 , for each pixel in the image, the upper bound defect pixel value, U, the lower bound defect pixel value, L, and the visible pixel value, V, are received. At step  802 , a calculation is made for the values of C 1  and C 2  using the upper and lower bounds U and L. At step  804 , a determination is made as to whether the product of C 1  and C 2  is less than zero. If the decision is yes, the final correction C is set to zero at step  806 . If the decision is no at step  804 , a determination is made as to whether the value of C 1  is positive. If the decision is yes, the correction C is set to the value of C 1  at step  810 . If the decision at block  808  is no, the correction value of C is set to the value of C 2 . The corrected value for C is output at step  814 . At step  816  a decision is made as to whether any pixels remain. If remaining pixels are to be analyzed, the program returns to step  800 . 
     As was mentioned earlier, the bounded subtractor assumes the lower frequencies are absent from the signal operated on by the subtractor such that an estimate of zero is the best estimate in the presence of complete uncertainty. An analogy may be drawn to the stock market wherein the best estimate for tomorrow&#39;s price is zero change from today&#39;s price, not zero price. In the case of infrared surface defect correction, the lower frequencies are separated from the higher frequencies and corrected with a direct subtraction without bounding. The errors made will be minimal because most defects are very local and thus have little effect over a broad region, and in addition, even poor scanners perform well at low frequencies. The low frequency image so corrected is later added to the high frequency image corrected with the bounded subtractor to produce the final corrected image. 
     Such a frequency division is illustrated in FIG. 9. A signal  902  (FIG. 9 a ) is received that contains a defect  904 . The signal  902  is divided into a low frequency component  906  (FIG. 9 b ) and a high frequency component  908  (FIG. 9 c ). The high frequency component  908  may be found by subtracting the low frequency component  906  from the original signal  902 . Normally, the low frequency component  906  would be further processed by subtracting the low frequency component of the infrared channel (not shown) from it. 
     Within a region  910  (FIG. 9 d ), it is determined that there is a defect, and that the upper and lower bounds are so wide that the best estimate will be just zero. Accordingly, in this region the high frequency signal  908  is simply set to zero to produce the bounded high frequency signal  912 . Finally, signals  912  and  906  are added to produce the corrected signal  914  (FIG. 9 e ). It may be seen that by splitting out the lower frequencies, the zeroing of the higher frequencies has merely muffled the defect, which in the absence of any better estimate, is the best compromise. In practice, the nulling subtractor would work within a narrower range off of zero for a better cancellation of the defect. However, it is illustrated that even in the extreme case of totally zeroing the high frequency signal, the result is reasonable. 
     The bounded subtractor works well at totally eradicating the effects of a defect in a drive to zero; however, a primary limitation of the bounded subtractor as thus far presented is illustrated in FIG. 10. A portion  1002  of a visible image may show strands of Shirley&#39;s hair  1004 , but in addition show an undesired scratch  1006  on the film. The scratch  1008  also records in the infrared record  1010  of the corresponding portion of the image. The image of the scratch  1008  is processed by the upper and lower bound functions  1012  and  1014  to produce the upper and lower bound corrector images  1020  and  1022  as previously described. These bounds guide the bounded subtractor  1026  to remove the effects of the defect. Depending on the looseness of the bounds set by functional blocks  1012  and  1014 , some of the desired image will also be subtracted in an attempt to make sure the defect has been eradicated. The disadvantage of the present method as described thus far is that this overcorrection may leave gaps or smudged spots  1030  in Shirley&#39;s hair  1032  of the output portion of the corrected image  1034 . 
     FIG. 11 teaches defect correction in a transform space so as to eliminate or reduce the problem of overcorrection. A portion of an image is received as block  1102  containing again strands of Shirley&#39;s hair  1104  and a defect scratch  1106 . The image is assumed to be received in logarithmic space to permit linear processing as described earlier; however, it is not necessary to filter out the lower frequencies as before because the transform will inherently segregate the low frequency components. 
     A Discrete Cosine Transform, commonly known as a DCT, will be used for illustration and for the preferred embodiment. Algorithms to derive a DCT are very well known in the art as this transform is at the heart of MPEG (Motion Picture Expert Group) and JPEG (Joint Photographic Expert Group) compressions used in image libraries and digital television, and so the derivation of a DCT will not be given here. In addition, there are many other transforms each with its own advantages and disadvantages, and the use of a DCT for illustration should not be considered a limitation. For example, the Fourier Transform will give better discernment of angles compared to the DCT; however, it has problems with boundary conditions. The Hademard Transform has certain computational simplicities. 
     Turning now to FIG. 11, the visible image portion  1102  is processed in block  1110  by a DCT to produce a visible transformed block  1112 . In the preferred embodiment, the image portion  1102  is assumed to consist of 8×8 pixels, and therefore the transform contains 8×8 elements. This is a common size used in many compression algorithms, and is found to work well. It is used in this illustration for convenience, and not by way of limitation. In the DCT, by convention the lowest frequency element is at the top left  1114 . This element contains the DC (Direct Current), which is the average of all pixels in the image block  1102 . This inherent separation of this low frequency term means that explicit frequency division is not needed in the DCT transform space. Similarly, the infrared image portion  1116  and defect scratch  1118  are processed by a DCT  1120  to produce an infrared transformed block  1122 . 
     Moving to the right from the DC term  1114  are the spectral components  1124  of the vertical strands of hair  1104 . Moving down from the DC term  1114  are the spectral components  1126  of the scratch  1106 . This simple illustration spotlights the power of a transformer to isolate a defect from image detail by segregating specific details both by frequency and by angle. By operating in transform space, the bounded subtractor  1128  is able to completely subtract out the defect component  1122  between the upper and lower bound functions  1130  and  1132  which produce corrected images  1134  and  1136  without touching the desired image components  1124  at image  1138 . After taking the inverse DCT at  1140 , the strands of hair  1142  are correctly reproduced with no gaps and no defects. In effect, the image has been smudged along the lines of the image so the smudging is almost unnoticed. 
     As was mentioned, the preferred embodiment uses a block size of 8×8. A smaller block size will give better discernment based on position but poorer discernment based on frequency and angle, while a larger block will give opposite results. The block size of 8×8 has been found to be an optimum compromise but is not offered as a limitation. 
     FIG. 12 further describes the details of operation in a transform space. An input visible image  1202  is broken into many blocks, which may be divided into 8×8 pixels as illustrated. These blocks may overlap to reduce boundary effects. A specific block  1204  is selected for correction. The logarithm of each pixel in the block is taken, and the DCT performed on the block to produce the transformed block  1210 , as described earlier. 
     A defect which may occur in some scanners is misregistration of the infrared and visible images. The effects of this can be compensated as is now shown. The infrared image  1220  is also divided into multiple blocks, and the corresponding block  1222  is selected, but a wider area  1224  around the block is utilized. An example would be a 10×10 region. After taking the logarithm of each pixel in the region, several 8×8 regions are selected from this larger 10×10 region. For example, a center region  1226  may be taken, an upper region  1228  shown by the dotted line, a lower region, a left region, and a right region. The DCT is taken on each of these selected regions. 
     Each of the regions just mentioned produces a suite  1230  of DCT blocks. The perfect correction may be at a fractional pixel of displacement; therefore, none may match exactly, but a subset of these DCT values will give a good estimate. In the illustration, each infrared DCT in the suite  1230  of DCTs is compared with the visible DCT  1210  to test the degree of match using the suite of function blocks  1232 . In one embodiment, the three with the best match are used to determine the upper and lower bounds. In another implementation, each is factored in with a weighted average based on the exactness of the match. In any case, this suite  1230  of DCTs is used by function block  1233  to generate an upper and lower bounds  1234  and  1236  for each element of the DCT block, and these bounds used by the bounded subtractor  1238  to generate the corrected DCT block  1240 . After taking the inverse DCT to generate block  1242 , and the inverse logarithm, the corrected image block  1248  is placed in the output corrected image  1250 . 
     FIG. 13 teaches how the suite of function blocks  1232  of FIG. 12 may take the correlation. A classic mathematical correlation takes the sum of the products of all terms of the two blocks being correlated. However, in the case of this invention, the visible record may contain very large values induced by image details at lower frequencies, not echoed in the infrared record, that could overpower valid defect details at higher frequencies. FIG. 13 teaches a method of weighting each element with a magnitude corresponding only to the infrared component, which bears the defect detail that will appear in both the infrared and visible images. The multiplication uses only the sign of the visible element with the value for the corresponding defect element. This prevents a huge magnitude of the visible element from overpowering other terms. In an alternate embodiment, the visible and infrared terms are multiplied similar to a classic correlation; however, the visible term is limited in magnitude to be less than or equal to the infrared term magnitude. 
     Referring again to FIG. 13, an image block is obtained at step  1300 . For each block, at step  1302 , the 8×8 elements of the DCT visible block are received. At step  1304 , the 8×8 elements of the DCT defect block are received. The correlation is initially set to zero at block  1306 . For each of the 8×8 elements, a new correlation value is calculated at step  1308 . The new correlation is equal to the previous value for the correlation plus the sign of the visible element multiplied by the corresponding defect element. The correlation for each block is output at step  1312 . If any blocks remain at Step  1314 , a new block is obtained at step  1300 . If not, the calculation is completed. 
     FIG. 14 illustrates graphically a way of calculating the upper and lower bounds. In this figure, only one-dimensional signals are shown for simplicity. These may represent a single row  1402  (FIG. 14 a ) of a DCT block  1404 . The end of this row closest to the DC term  1406  would represent lower frequencies, and the other end would represent higher frequencies. In two-dimensional space, the distance from the DC term  1406  to any specific element would measure the frequency of that element. 
     As discussed before, the three displaced infrared DCT transforms  1410 ,  1412 , and  1414  (FIG. 14 b ) with the highest correlations to the visible DCT transform may be received. The range of these three transforms may give an upper and lower bound  1420  and  1422  (FIG. 14 c ) for each element along the row of the DCT. The DC term may be handled as a special case wherein the upper and lower bounds are set the same, and equal to the average of the DC term of the three blocks. Thus, the DC term is excluded from processing by the bounded subtractor because the DC term represents average brightness and cannot be set toward zero as a default nulling. 
     The next step is to extend these bounds, recopied as dotted lines  1420  and  1422  (FIG. 14 d ) to wider bounds  1426  and  1428  in accordance with expected frequency response rolloff and variations in the actual scanner versus an ideal scanner. In a region  1430  wherein the upper and lower bounds are on opposite sides of zero, both would be multiplied by a constant greater than one that may be called “upper extend” in order to pull the curves  1426  and  1428  further apart by pushing them both away from zero. Conversely, in a region  1432  wherein the upper and lower bounds are on the same side of zero, the one closest to zero would need to be multiplied by a second constant less than one that may be called “lower extend” in order again to pull the curves farther apart, this time by pulling the one closest to zero toward zero, as shown in FIG. 14 e . A typical value for “upper extend” is 1.5, and a typical value for “lower extend” is 0.5. 
     The constants “upper extend” and “lower extend” are typically constants that are dependent on frequency, and may vary from equality at the DC term to widely divergent values at the highest frequency farthest from the DC term. In this case, “upper extend” may vary linearly for 1.0 at DC to 2.0 at highest frequency terms farthest from DC, and “lower extend” may vary linearly from 1.0 at DC to 0.0 at the highest frequency terms. Also, the constants “upper extend” and “lower extend” are typically greater and less than unity respectively, but they do not need to be. For example, if it is known that a scanner responds at a particular frequency with only 50% modulation in the infrared spectrum as compared to the visible spectrum, then both upper and lower extends could be multiplied by 1/50%=2 to compensate, which may make the lower extend greater than unity. 
     Finally, some scanners do not respond effectively to the higher frequency details in the infrared range, and with these scanners it is necessary to use the lower frequency details in the infrared spectrum to predict a range to correct in the high frequencies. In effect, the high frequencies simply get smudged in proportion to the defect content in the lower frequencies. 
     To practice this high frequency smudging, the average content of lower frequency defects is found by averaging the absolute value of lower frequency elements of the infrared DCT. This value is used to set upper and lower bounds  1426  and  1428  below which the final bounds  1426  and  1428  below which the final bounds are not allowed to fall. Conversely, the new range extensions  1430  and  1432  can be added to the upper and lower bounds  1426  and  1428  which for such scanners presumably approach zero at high spatial frequencies in the infrared. 
     FIG. 15 is a block diagram of the teachings of FIG.  14 . At step  1500 , the three offset defects DCT&#39;s with highest correlation to visible DCT are obtained as values DCT  1 , DCT 2 , and DCT 3 . The upper and lower extends for each block are received at step  1502 . A new element in the block is obtained at step  1504 . For each element, x, of the 8×8 elements, a calculation is made at step  1506  to calculate DCT Max (x), and DCT Min (x). DCT Max (x) is equal to the maximum of DCT  1 (x), DCT  2 (x), and DCT  3 (x). DCT Min (x) is equal to the minimum of DCT  1 (x), DCT  2 (x), and DCT  3 (x). At step  1508 , a decision is made as to whether both DCT Max (x) and DCT Min (x) is positive. If the decision is yes, at step  1510 , DCT Max (x) is set to the upper extend (x), U. DCT Min (x) is set to the lower extend (x), L. If the decision at step  1508  is no, a decision is made at step  1512  to determine whether both DCT Max (x) and DCT Min (x) is negative. If the decision is yes, at step  1514 , DCT Min (x) is set to the upper extend (x), U. DCT Max (x) is set to the lower extend (x), L. If the decision at step  1512  is no, meaning that DCT Max (x) and DCT Min (x) are of opposite signs, DCT Max (x) is set to the upper extend (x), U and DCT Min (x) is set to the lower extend (x), L. A decision is then made at step  1518  to determine if there are any elements remaining to be analyzed. If the decision is yes, the process continues with step  1504 . If the decision is no, the average of the lower frequency elements excluding DC, for each high frequency element x is calculated. DCT Max (x) is then recalculated at step  1522  as the maximum of DCT Max (x) and a positive constant times the lower frequencies. DCT Min (x) is recalculated at step  1522  as equal to the minimum of DCT Min (x) and a negative constant times the average of the lower frequencies. 
     Whereas the present invention has been described with respect to specific embodiments thereof, it will be understood that various changes and modifications will be suggested to one skilled in the art, and it is intended to encompass to such changes and modifications as fall within the scope of the appended claims.