CA2397817C - Flat-field panel flattening, and panel connecting methods - Google Patents
Flat-field panel flattening, and panel connecting methods Download PDFInfo
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- 238000009499 grossing Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 description 6
- 230000035945 sensitivity Effects 0.000 description 5
- 238000012935 Averaging Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000005286 illumination Methods 0.000 description 3
- 238000012417 linear regression Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
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- 238000003491 array Methods 0.000 description 1
- 239000012620 biological material Substances 0.000 description 1
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- 238000010191 image analysis Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
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- 238000003672 processing method Methods 0.000 description 1
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- G06T5/70—
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
- H04N25/40—Extracting pixel data from image sensors by controlling scanning circuits, e.g. by modifying the number of pixels sampled or to be sampled
- H04N25/41—Extracting pixel data from a plurality of image sensors simultaneously picking up an image, e.g. for increasing the field of view by combining the outputs of a plurality of sensors
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
- H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
- H04N25/61—Noise processing, e.g. detecting, correcting, reducing or removing noise the noise originating only from the lens unit, e.g. flare, shading, vignetting or "cos4"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10064—Fluorescence image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30072—Microarray; Biochip, DNA array; Well plate
Abstract
A plurality of panels are assembled into a single image. Each of the panels may have different intensities throughout the panel, as well as non-uniformities between panels. The panels are modified using flat-field calibration, panel flattening, and panel connecting techniques. These techniques correct for non-uniformities and provide a cleaner, single image.
Description
FLAT-FIELD, PANEL FLATTENING, AND PANEL CONNECTING METHODS
TECHNICAL FIELD
This invention relates to image analysis, and more particularly to using correcting for non-uniformities among several panels of a single image.
BACKGROUND
Biomedical research has made rapid progress based on sequential processing of biological samples. Sequential processing techniques have resulted in important discoveries in a variety of biologically related fields, including, among others, genetics, biochemistry, immunology and enzymology. Historically, sequential processing involved the study of one or two biologically relevant molecules at the same time. These original sequential processing methods, however, were quite slow and tedious. Study of the required number of samples (up to tens of thousands) was time consuming and costly.
A breakthrough in the sequential processing of biological specimens occurred with the development of techniques of parallel processing of the biological specimens, using fluorescent marking. A plurality of samples are arranged in arrays, referred to herein as microarrays, of rows and columns into a field, on a substrate slide or similar member. The specimens on the slide are then biochemically processed in parallel. The specimen mclecules are fluorescently marked as a result of interactic-~ between the specimen molecule and other biological material. Such techniques enable the processing of a larce number of specimens very quickly.
Some applications for imaging require --wo apparently contradictory attributes: high-resolution and high-content.
The resolution requirement is driven by the need to have detail in the image that exceeds by at -~east 2X the information content of the object being images ;~he so called Nyquist Limit). The content requirement is driven by the need to have information over a large area. One method that addresses these needs is to acquire a plurality of individual images with high spatial resolution (panels) a-.d to collect these panels over adjacent areas so as to encompass the large desired area. The multiple panels can then be assembled into a single large image based on the relative location of the optics and the sample when each panel was ccllected. When assembling the plurality of panels into a single montage, a number of steps may be taken to correct for intensity non-uniformities within each panel (known herein as flat-field Calibration and Panel Flattening) as well as non-uniformities in the panel to panel intensities.
According to one aspect the invention provides a method of reducing field curvature in an image comprising:
obtaining an average curvature map of a plurality of image panels; and dividing each panel by the curvature map.
According to another aspect the invention provides a method of reducing discontinuities between adjacent panels in an image comprising: comparing a border of each panel on all sides to generate border intensity scaling values; and scaling a boundary of each panel to a point approximately midway between a current panel and an adjacent panel.
DESCRIPTION OF DRAWINGS
These and other features and advantages of the invention will become more apparent upon reading the following detailed description and upon reference to the accompanying drawings.
Figure 1 is a flat-field calibration map showing the overall curvature and offset maps according to one embodiment of the present invention.
Figure 2 is a close-up view of a 20x20 region of the inverse gain map and offset map of Figure 1.
TECHNICAL FIELD
This invention relates to image analysis, and more particularly to using correcting for non-uniformities among several panels of a single image.
BACKGROUND
Biomedical research has made rapid progress based on sequential processing of biological samples. Sequential processing techniques have resulted in important discoveries in a variety of biologically related fields, including, among others, genetics, biochemistry, immunology and enzymology. Historically, sequential processing involved the study of one or two biologically relevant molecules at the same time. These original sequential processing methods, however, were quite slow and tedious. Study of the required number of samples (up to tens of thousands) was time consuming and costly.
A breakthrough in the sequential processing of biological specimens occurred with the development of techniques of parallel processing of the biological specimens, using fluorescent marking. A plurality of samples are arranged in arrays, referred to herein as microarrays, of rows and columns into a field, on a substrate slide or similar member. The specimens on the slide are then biochemically processed in parallel. The specimen mclecules are fluorescently marked as a result of interactic-~ between the specimen molecule and other biological material. Such techniques enable the processing of a larce number of specimens very quickly.
Some applications for imaging require --wo apparently contradictory attributes: high-resolution and high-content.
The resolution requirement is driven by the need to have detail in the image that exceeds by at -~east 2X the information content of the object being images ;~he so called Nyquist Limit). The content requirement is driven by the need to have information over a large area. One method that addresses these needs is to acquire a plurality of individual images with high spatial resolution (panels) a-.d to collect these panels over adjacent areas so as to encompass the large desired area. The multiple panels can then be assembled into a single large image based on the relative location of the optics and the sample when each panel was ccllected. When assembling the plurality of panels into a single montage, a number of steps may be taken to correct for intensity non-uniformities within each panel (known herein as flat-field Calibration and Panel Flattening) as well as non-uniformities in the panel to panel intensities.
According to one aspect the invention provides a method of reducing field curvature in an image comprising:
obtaining an average curvature map of a plurality of image panels; and dividing each panel by the curvature map.
According to another aspect the invention provides a method of reducing discontinuities between adjacent panels in an image comprising: comparing a border of each panel on all sides to generate border intensity scaling values; and scaling a boundary of each panel to a point approximately midway between a current panel and an adjacent panel.
DESCRIPTION OF DRAWINGS
These and other features and advantages of the invention will become more apparent upon reading the following detailed description and upon reference to the accompanying drawings.
Figure 1 is a flat-field calibration map showing the overall curvature and offset maps according to one embodiment of the present invention.
Figure 2 is a close-up view of a 20x20 region of the inverse gain map and offset map of Figure 1.
Figure 3 illustrates an image before and after applying curvature flattening according to one embodiment of the present invention.
DETAILED DESCRIPTION
To create a large image, a plurality of smaller images are collected by a detector and assembled into a single large image. Each of the plurality of smaller images collected by the detector may be affected by a combination of the non-uniform optics and detector response. In the case of - 3a -the optics, illumination vignetting and collection vignetting introduce a substantiai intensity curvature to the images collected by the detector. Non-uniform detector response comes in the form of gain and offset differences among all the detector elements.
To correct for these errors, a series of images are acquired that range from dark current (no exposure) to near full-well. Linear regression of each pixel in the detector yields a slope (gain) and intercept (offset). That is, for each pixel the following equation is solved for m and b:
Measured image = Desired image * m + b Flat-field calibration is then accomplished with the following calculation (again for each pixel):
Desired_image = (Measured_image - offset_map) / gain_map Where m has been replaced with "gain_map" and b with "offset map".
The gain and offset maps correct for the illumination optics, collection optics, and detector non-uniformity at the same time.
Flat-field calibration maps that correct the image field curvature and offset problem do so at the expense of adding noise to the image. Both maps contain measurement noise that is then passed on to the calibrated image. The gain map contains noise that is mostly photon counting noise ("shot noise"), whereas the offset map is dominated by the electronic read-noise of the CCD camera.
To correct for the offset map noise, the average dark current image (no exposure) may be used instead of the linear regression result. That is, the offset map used to flat-field images is the average of many dark current images rather than the intercept calculated by the linear regression.
Experience has shown that the intercept is inherently noisy (the intercept is measured at the low signal-to-noise part of io the camera range). Use of the calculated offset map reduces the sensitivity of the instrument by increasing the baseline noise. The offset map shown in Figures 1 and 2 are the average dark current. The calculated intercept would have about double the noise of the average dark current.
Averaging multiple frames for each measurement improves the signal-to-noise of the data and reduces the noise in the resulting gain and offset maps (in the event that the calculated offset map is used for flat-fielding).
Another technique is to smooth the gain map with a low-pass filter.
Perfectly uniform flat-field calibration slides are nearly impossible to fabricate. Non-uniform fluorescence is typical even with very carefully prepared slides. However, moving the calibration slide during camera exposure averages non-uniform fluorescent response of the slide. Flat-field calibration maps can be generated from significan~ly lower quality calibration slides.
Figures 1 and 2 illustrate flat-field calibration maps made from uniformly fluorescent calibration slides. The gain map 105, 205 contains approximately 0.3% noise whereas the offset map 110, 210 contributes 1.24 counts (gain correction is multiplicative, offset is additive).
Although flat-field calibration is an effective technique, the technique introduces noise. Cleaning the flat-field calibration maps could yield substantial improvements in image quality. In particular, further reduction of offset map noise would improve low-end sensitivity. The read-noise in the CCD camera used to collect the maps above has about 1.77 counts of read-noise. Adding the offset map noise (in quadrature) yields about 2.2 counts of baseline noise, a 24%
increase.
Another problem is that the intensity curvature of the panels creates a visible artifact. Figure 3 illustrates an image 300 without any curvature correction. A combination of illumination vignetting and collection vignetting leads to more brightness or higher collection efficiency, respectively, in the center of the field-of-view. Even when flat-fielding techniques have been applied to the panels, a variety of factors contribute to a residual curvature. For instance, lamp fluctuation, camera bias instability change the general intensity level of the acquired image and affect the standard flat-fielding calculation, which is:
flat image = (acquired_image - offset_map) / gain_map.
Small errors in the offset map cause the gain map (which is usually curved) to introduce a field curvature. The more curvature that exists in the acquired image, the greater the potential for residual curvature.
Because the intensity curvature is typically consistent from one panel to the next, averaging the intensity profile of each panel gives an average curvature map.
Dividing each panel by the curvature map is then a way to flatten the intensity curvature that is consistent among all panels. Normalizing the curvature map by the average intensity, or similar value, of the curvature map allows the calculation to be performed without altering the net intensity scale of the image.
One example of how to average the intensity profile of each panel is to perform the following procedure for each pixel in each panel. First, if the pixel in the current panel is not signal, apply the following equations:
Accumulator map = accumulator_map + pixel_intensity Accumulation_counter_map = accumulation_counter_map + 1 Second, for all pixels within the accumulator_map, calculate the curvature map using the following technique:
If counter map is greater than 0 Curvature map = accumulator_map /
accumulation counter map Otherwise Curvature map = average of neighboring curvature values io This creates a curvature flattening map that is defined as:
Curvature flattener = 1 / curvature map The procedure may be refined in several manners.
First, the curvature map may be smoothed to reduce the sensitivity to noise and spurious signals in the average curvature image. Second, only the pixels from each panel that are not significantly above the background intensity may be averaged. A histogram of each panel is used to distinguish background areas (desired) from image signals (undesired). A
map of the number of pixels added to each point in the curvature map is then required to calculate the average since not all panels contribute information to each pixel in the curvature map. Pixels that contain no information can be synthesized from the average of neighboring pixels. Third, the curvature map may be curve-fitted using a weighting scheme that emphasizes relatively low intensity values. Curve-fitting would be useful for reducing noise. The goal of curve-fitting is to measure only the background curvature and reduce the influence of the image signal. Other refinements include averaging lots of small panels reduces sensitivity to image signal corruption and over-scanning the desired image area to provide more panels for averaging and panels that contain only the background intensity curvature.
Another problem with combining a plurality of small images to form one large image is that small discontinuities between adjacent panels become visible. Intensity differences of 1-2 counts are readily detected by the human eye, even in the presence of 1-2 counts of random noise and when important information is much more intense. The remaining discontinuity create a visible stitching artifact. Examples of the discontinuities may be seen in the image 300 of Figure 3.
To correct this problem, a panel edge connection technique is performed. In this technique, the border of each panel is compared with all neighbors to the left, right, top, and bottom. This comparison generates border intensity scaling values for the entire boundary of each panel. The boundary may then be scaled so that the result is half way between the boundary of the current panel and the adjacent panel. The intensities are then connected at the half-way point between the adjacent border intensities. The boundary scaling may be applied to each pixel in the panel based on the distance from the four boundaries. A weighted combination of the scaling factors is used such that a continuous intensity ramp is applied from one boundary to the next. (In the middle of the image, the scaling factor should be the average of the left, right, top, and bottom scaling factors.) Some examples of the weighting methods include inverse square weighting and inverse weighting. These techniques may be implemented using the following formulas:
Inverse square weighting:
Left weight = 1 / (i + 1)~2 Right weight = 1/(nx - i + 1)~2 Bottom weight = 1 / (j + 1)~2 Top weight = 1/(ny - j + 1)~2 Inverse weighting:
Left weight = 1 / (i + 1) Right_weight = 1/(nx - i + 1) Bottom weight = 1/( j + 1) Top weight = 1 / (ny - j + 1) Total_weight = Left_weight + Right_weight +
Top_weight + Bottom_weight Scaling Factors:
Leftscale (j ) = 1-~ * [Left_border (j ) +
Right_border_of_left_panel(j)]/Left_border(j) Right_scale (j ) = =~2 * [Right_border (j ) +
Left_border_of_right_panel(j)]/Right_border(j) Top_scale (i) _ '~ * [Top_border (i) +
Bottom border of upper panel(i)]/Top border(i) Bottom_scale(i)= %z * [Bottom_border(i) +
Top_border_of_lower_panel(i)]/Bottom_border(i) Pixel(i,j) intensity scaling factor =
[Leftscale(j) * Left_weight + Right_scale(j)*
Right_weight + Bottom_scale(i)*Bottom_weight +
Top scale(i)*Top_weight] / Total_weight Definitions:
nx Number of pixel columns ny Number of pixel rows i Column number (0 based) j Row number (0 based) Both connection and curvature flattening are important for panels with significant background intensity.
An image having curvature flattening is shown in Figure 3.
Further refinements include median filtering the boundary scaling values to reduce sensitivity to outliers.
Misalignment of the panels causes miscalculation of the scaling factors. The miscalculation is significant when bright (or dark) spots do not overlap along the borders of adjacent panels. Additionally, smoothing of the median filtered boundary scaling values may be used to remove spikes caused by alignment problems. Finally, the boundary scaling values may be curve-fit to find the general trend and avoid noise and misalignment.
DETAILED DESCRIPTION
To create a large image, a plurality of smaller images are collected by a detector and assembled into a single large image. Each of the plurality of smaller images collected by the detector may be affected by a combination of the non-uniform optics and detector response. In the case of - 3a -the optics, illumination vignetting and collection vignetting introduce a substantiai intensity curvature to the images collected by the detector. Non-uniform detector response comes in the form of gain and offset differences among all the detector elements.
To correct for these errors, a series of images are acquired that range from dark current (no exposure) to near full-well. Linear regression of each pixel in the detector yields a slope (gain) and intercept (offset). That is, for each pixel the following equation is solved for m and b:
Measured image = Desired image * m + b Flat-field calibration is then accomplished with the following calculation (again for each pixel):
Desired_image = (Measured_image - offset_map) / gain_map Where m has been replaced with "gain_map" and b with "offset map".
The gain and offset maps correct for the illumination optics, collection optics, and detector non-uniformity at the same time.
Flat-field calibration maps that correct the image field curvature and offset problem do so at the expense of adding noise to the image. Both maps contain measurement noise that is then passed on to the calibrated image. The gain map contains noise that is mostly photon counting noise ("shot noise"), whereas the offset map is dominated by the electronic read-noise of the CCD camera.
To correct for the offset map noise, the average dark current image (no exposure) may be used instead of the linear regression result. That is, the offset map used to flat-field images is the average of many dark current images rather than the intercept calculated by the linear regression.
Experience has shown that the intercept is inherently noisy (the intercept is measured at the low signal-to-noise part of io the camera range). Use of the calculated offset map reduces the sensitivity of the instrument by increasing the baseline noise. The offset map shown in Figures 1 and 2 are the average dark current. The calculated intercept would have about double the noise of the average dark current.
Averaging multiple frames for each measurement improves the signal-to-noise of the data and reduces the noise in the resulting gain and offset maps (in the event that the calculated offset map is used for flat-fielding).
Another technique is to smooth the gain map with a low-pass filter.
Perfectly uniform flat-field calibration slides are nearly impossible to fabricate. Non-uniform fluorescence is typical even with very carefully prepared slides. However, moving the calibration slide during camera exposure averages non-uniform fluorescent response of the slide. Flat-field calibration maps can be generated from significan~ly lower quality calibration slides.
Figures 1 and 2 illustrate flat-field calibration maps made from uniformly fluorescent calibration slides. The gain map 105, 205 contains approximately 0.3% noise whereas the offset map 110, 210 contributes 1.24 counts (gain correction is multiplicative, offset is additive).
Although flat-field calibration is an effective technique, the technique introduces noise. Cleaning the flat-field calibration maps could yield substantial improvements in image quality. In particular, further reduction of offset map noise would improve low-end sensitivity. The read-noise in the CCD camera used to collect the maps above has about 1.77 counts of read-noise. Adding the offset map noise (in quadrature) yields about 2.2 counts of baseline noise, a 24%
increase.
Another problem is that the intensity curvature of the panels creates a visible artifact. Figure 3 illustrates an image 300 without any curvature correction. A combination of illumination vignetting and collection vignetting leads to more brightness or higher collection efficiency, respectively, in the center of the field-of-view. Even when flat-fielding techniques have been applied to the panels, a variety of factors contribute to a residual curvature. For instance, lamp fluctuation, camera bias instability change the general intensity level of the acquired image and affect the standard flat-fielding calculation, which is:
flat image = (acquired_image - offset_map) / gain_map.
Small errors in the offset map cause the gain map (which is usually curved) to introduce a field curvature. The more curvature that exists in the acquired image, the greater the potential for residual curvature.
Because the intensity curvature is typically consistent from one panel to the next, averaging the intensity profile of each panel gives an average curvature map.
Dividing each panel by the curvature map is then a way to flatten the intensity curvature that is consistent among all panels. Normalizing the curvature map by the average intensity, or similar value, of the curvature map allows the calculation to be performed without altering the net intensity scale of the image.
One example of how to average the intensity profile of each panel is to perform the following procedure for each pixel in each panel. First, if the pixel in the current panel is not signal, apply the following equations:
Accumulator map = accumulator_map + pixel_intensity Accumulation_counter_map = accumulation_counter_map + 1 Second, for all pixels within the accumulator_map, calculate the curvature map using the following technique:
If counter map is greater than 0 Curvature map = accumulator_map /
accumulation counter map Otherwise Curvature map = average of neighboring curvature values io This creates a curvature flattening map that is defined as:
Curvature flattener = 1 / curvature map The procedure may be refined in several manners.
First, the curvature map may be smoothed to reduce the sensitivity to noise and spurious signals in the average curvature image. Second, only the pixels from each panel that are not significantly above the background intensity may be averaged. A histogram of each panel is used to distinguish background areas (desired) from image signals (undesired). A
map of the number of pixels added to each point in the curvature map is then required to calculate the average since not all panels contribute information to each pixel in the curvature map. Pixels that contain no information can be synthesized from the average of neighboring pixels. Third, the curvature map may be curve-fitted using a weighting scheme that emphasizes relatively low intensity values. Curve-fitting would be useful for reducing noise. The goal of curve-fitting is to measure only the background curvature and reduce the influence of the image signal. Other refinements include averaging lots of small panels reduces sensitivity to image signal corruption and over-scanning the desired image area to provide more panels for averaging and panels that contain only the background intensity curvature.
Another problem with combining a plurality of small images to form one large image is that small discontinuities between adjacent panels become visible. Intensity differences of 1-2 counts are readily detected by the human eye, even in the presence of 1-2 counts of random noise and when important information is much more intense. The remaining discontinuity create a visible stitching artifact. Examples of the discontinuities may be seen in the image 300 of Figure 3.
To correct this problem, a panel edge connection technique is performed. In this technique, the border of each panel is compared with all neighbors to the left, right, top, and bottom. This comparison generates border intensity scaling values for the entire boundary of each panel. The boundary may then be scaled so that the result is half way between the boundary of the current panel and the adjacent panel. The intensities are then connected at the half-way point between the adjacent border intensities. The boundary scaling may be applied to each pixel in the panel based on the distance from the four boundaries. A weighted combination of the scaling factors is used such that a continuous intensity ramp is applied from one boundary to the next. (In the middle of the image, the scaling factor should be the average of the left, right, top, and bottom scaling factors.) Some examples of the weighting methods include inverse square weighting and inverse weighting. These techniques may be implemented using the following formulas:
Inverse square weighting:
Left weight = 1 / (i + 1)~2 Right weight = 1/(nx - i + 1)~2 Bottom weight = 1 / (j + 1)~2 Top weight = 1/(ny - j + 1)~2 Inverse weighting:
Left weight = 1 / (i + 1) Right_weight = 1/(nx - i + 1) Bottom weight = 1/( j + 1) Top weight = 1 / (ny - j + 1) Total_weight = Left_weight + Right_weight +
Top_weight + Bottom_weight Scaling Factors:
Leftscale (j ) = 1-~ * [Left_border (j ) +
Right_border_of_left_panel(j)]/Left_border(j) Right_scale (j ) = =~2 * [Right_border (j ) +
Left_border_of_right_panel(j)]/Right_border(j) Top_scale (i) _ '~ * [Top_border (i) +
Bottom border of upper panel(i)]/Top border(i) Bottom_scale(i)= %z * [Bottom_border(i) +
Top_border_of_lower_panel(i)]/Bottom_border(i) Pixel(i,j) intensity scaling factor =
[Leftscale(j) * Left_weight + Right_scale(j)*
Right_weight + Bottom_scale(i)*Bottom_weight +
Top scale(i)*Top_weight] / Total_weight Definitions:
nx Number of pixel columns ny Number of pixel rows i Column number (0 based) j Row number (0 based) Both connection and curvature flattening are important for panels with significant background intensity.
An image having curvature flattening is shown in Figure 3.
Further refinements include median filtering the boundary scaling values to reduce sensitivity to outliers.
Misalignment of the panels causes miscalculation of the scaling factors. The miscalculation is significant when bright (or dark) spots do not overlap along the borders of adjacent panels. Additionally, smoothing of the median filtered boundary scaling values may be used to remove spikes caused by alignment problems. Finally, the boundary scaling values may be curve-fit to find the general trend and avoid noise and misalignment.
WO 01/55964 PCT/USOl/02639 Numerous variations and modifications of the invention will become readily apparent to those skilled in the art. Accordingly, the invention may be embodied in other specific forms without departing from its spirit or essential characteristics.
Claims (8)
1. A method of reducing field curvature in an image comprising:
obtaining an average curvature map of a plurality of image panels; and dividing each panel by the curvature map.
obtaining an average curvature map of a plurality of image panels; and dividing each panel by the curvature map.
2. The method of Claim 1, further comprising normalizing the curvature map by the average intensity of the curvature map.
3. The method of Claim 1, further comprising smoothing the curvature map.
4. The method of Claim 1, further comprising using only pixels below signal threshold to obtain the average curvature map.
5. The method of Claim 1, further comprising reducing noise in the image by curve-fitting the image pixels.
6. A method of reducing discontinuities between adjacent panels in an image comprising:
comparing a border of each panel on all sides to generate border intensity scaling values; and scaling a boundary of each panel to a point approximately midway between a current panel and an adjacent panel.
comparing a border of each panel on all sides to generate border intensity scaling values; and scaling a boundary of each panel to a point approximately midway between a current panel and an adjacent panel.
7. The method of Claim 6, further comprising scaling the boundary of each panel using an inverse square weighting.
8. The method of Claim 6, further comprising scaling the boundary of each panel using an inverse weighting.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US17847600P | 2000-01-27 | 2000-01-27 | |
| US60/178,476 | 2000-01-27 | ||
| PCT/US2001/002639 WO2001055964A2 (en) | 2000-01-27 | 2001-01-26 | Flat-field panel flattening, and panel connecting methods |
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| Publication Number | Publication Date |
|---|---|
| CA2397817A1 CA2397817A1 (en) | 2001-08-02 |
| CA2397817C true CA2397817C (en) | 2008-08-12 |
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|---|---|---|---|
| CA002397817A Expired - Fee Related CA2397817C (en) | 2000-01-27 | 2001-01-26 | Flat-field panel flattening, and panel connecting methods |
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|---|---|
| US (3) | US20010038717A1 (en) |
| EP (1) | EP1254430B1 (en) |
| JP (1) | JP4278901B2 (en) |
| AT (1) | ATE475952T1 (en) |
| AU (1) | AU2001234588A1 (en) |
| CA (1) | CA2397817C (en) |
| DE (1) | DE60142678D1 (en) |
| WO (1) | WO2001055964A2 (en) |
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| US20050084175A1 (en) * | 2003-10-16 | 2005-04-21 | Olszak Artur G. | Large-area imaging by stitching with array microscope |
| JP2005181244A (en) | 2003-12-24 | 2005-07-07 | Yokogawa Electric Corp | Correction method of quantity-of-light distribution, and biochip reader |
| US7634152B2 (en) * | 2005-03-07 | 2009-12-15 | Hewlett-Packard Development Company, L.P. | System and method for correcting image vignetting |
| US7733357B2 (en) * | 2006-01-13 | 2010-06-08 | Hewlett-Packard Development Company, L.P. | Display system |
| WO2009037817A1 (en) * | 2007-09-19 | 2009-03-26 | Panasonic Corporation | Contour correcting device, contour correcting method and video display device |
| US8655043B2 (en) * | 2008-05-16 | 2014-02-18 | Huron Technologies International Inc. | Imaging system with dynamic range maximization |
| CN107454971B (en) * | 2017-07-17 | 2019-01-25 | 深圳市汇顶科技股份有限公司 | Optical sensor method for determining correction parameters, biological characteristic detection device and electric terminal |
| US20220374641A1 (en) * | 2021-05-21 | 2022-11-24 | Ford Global Technologies, Llc | Camera tampering detection |
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| US4979824A (en) * | 1989-05-26 | 1990-12-25 | Board Of Trustees Of The Leland Stanford Junior University | High sensitivity fluorescent single particle and single molecule detection apparatus and method |
| WO1996010237A1 (en) * | 1994-09-20 | 1996-04-04 | Neopath, Inc. | Biological specimen analysis system processing integrity checking apparatus |
| JP3377323B2 (en) | 1995-02-09 | 2003-02-17 | 株式会社モリタ製作所 | Medical X-ray equipment |
| JPH10504166A (en) | 1995-06-16 | 1998-04-14 | フィリップス エレクトロニクス エヌ ベー | Method and apparatus for synthesizing image from sub-image |
| US6111596A (en) * | 1995-12-29 | 2000-08-29 | Lucent Technologies Inc. | Gain and offset correction for efficient stereoscopic coding and improved display |
| US5675513A (en) * | 1996-02-16 | 1997-10-07 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Method of calibrating an interferometer and reducing its systematic noise |
| US6075905A (en) * | 1996-07-17 | 2000-06-13 | Sarnoff Corporation | Method and apparatus for mosaic image construction |
| JPH10145595A (en) | 1996-11-11 | 1998-05-29 | Konica Corp | Method for deciding gradation adjusting quantity in image pickup unit and image pickup unit |
| US6128108A (en) * | 1997-09-03 | 2000-10-03 | Mgi Software Corporation | Method and system for compositing images |
| JPH11196299A (en) | 1997-12-26 | 1999-07-21 | Minolta Co Ltd | Image pickup device |
| US6434265B1 (en) * | 1998-09-25 | 2002-08-13 | Apple Computers, Inc. | Aligning rectilinear images in 3D through projective registration and calibration |
| US6101238A (en) * | 1998-11-25 | 2000-08-08 | Siemens Corporate Research, Inc. | System for generating a compound x-ray image for diagnosis |
| US6556690B1 (en) * | 1999-06-17 | 2003-04-29 | Eastman Kodak Company | Articles bearing invisible encodements on curved surfaces |
| US6507665B1 (en) * | 1999-08-25 | 2003-01-14 | Eastman Kodak Company | Method for creating environment map containing information extracted from stereo image pairs |
| US6556960B1 (en) * | 1999-09-01 | 2003-04-29 | Microsoft Corporation | Variational inference engine for probabilistic graphical models |
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| CA2397817A1 (en) | 2001-08-02 |
| EP1254430B1 (en) | 2010-07-28 |
| JP4278901B2 (en) | 2009-06-17 |
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| JP2003521075A (en) | 2003-07-08 |
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