System and method for sharpening vector-valued digital images

• US 20060182364A1
• Filed: 02/16/2005
• Published: 08/17/2006
• Est. Priority Date: 02/16/2005
• Status: Abandoned Application

• Current Assignee: Sozotek, Inc.
• Original Assignee: Sozotek, Inc.
• Inventors: John, George
• Application Number:
  US11/059,009
• Publication Number:
  US 20060182364A1
• Time in Patent Office: Days
• US Class Current: 382/260
• CPC Class Codes: G06T 5/75 Unsharp masking

Abstract

Sharpening multi-spectral digital images without increasing noise is accomplished by filtering vector values rather than independent scalar values. A low-pass filter is performed on image A to obtain a blurred image B1 with noise and signal suppressed. The resulting blurred image B1 is subtracted from the original image A to produce a high frequency band C1 that contains noise and signal. Vector difference mean filtering is performed on the original image A to produce a filtered image B2 with noise suppressed. The filtered image B2 is subtracted from the original image A to produce a noise band C2 that contains noise with very little signal. The noise band C2 is subtracted from the high frequency band C1 to produce a signal band D that contains the signal. The signal band D is then added to the filtered image B2 to further enhance detail in the noise filtered band.

Claims

1. An automated method for sharpening a digitized, vector-valued, multi-spectral image A, the image comprising a plurality of pixels, the pixels each having vector values comprising a plurality of spectral components, the method comprising the computer-implemented steps of

performing a low-pass filter of image A to obtain a blurred image B1 with noise and signal suppressed;

subtracting the resulting blurred image B1 from the original image A to produce a high frequency band C1 that contains noise and signal;

using vector difference mean filtering on the original image A to produce a filtered image B2 with noise suppressed, such that the vector difference mean filtering filters the image A according to the vector values of image A;

subtracting the filtered image B2 from the original image A to produce a noise band C2, that contains noise with very little signal;

subtracting the noise band C2 from the high frequency band C1 to produce a signal band D that contains the signal, thereby recovering signal that was lost in the low pass filtering step; and

adding the signal band D to the filtered image B2 to further enhance detail in the noise filtered band.

2. The method of claim 1 wherein the low-pass filter is a spatial average filter.

3. The method of claim 1 wherein the vector values further comprise

a red scalar color component value for each of the plurality of pixels;

a green scalar color component value for each of the plurality of pixels; and

a blue scalar color component value for each of the plurality of pixels.

4. The method of claim 1 wherein the vector values further comprise

a red scalar color amplitude value for each of the plurality of pixels;

a red scalar color phase value for each of the plurality of pixels;

a green scalar color amplitude value for each of the plurality of pixels

a green scalar color phase value for each of the plurality of pixels;

a blue scalar color amplitude value for each of the plurality of pixels; and

a blue scalar color phase value for each of the plurality of pixels.

5. The method of claim 1 wherein the vector values further comprise

a cyan scalar color component value for each of the plurality of pixels;

a magenta scalar color component value for each of the plurality of pixels;

a yellow scalar color component value for each of the plurality of pixels; and

a black scalar color component value for each of the plurality of pixels.

6. The method of claim 1 wherein the vector difference mean filtering further comprises defining a metric, such that the metric uses the vector values of a first pixel and a second pixel to determine a metric value for the pixel;

and

filtering each of the portion of the plurality of pixels within the image by designating a center pixel;

defining a sliding processing kernel relative to the center pixel, the kernel

comprising kernel pixels which include the center pixel and a plurality of pixels in proximity to the center pixel;

for each pixel associated with the sliding processing kernel

using the metric to compute the metric value for the pixel from the center pixel vector value and the pixel vector value,

comparing the metric value for the pixel to a threshold value for the kernel pixels in order to determine whether to include the pixel value in a filter calculation for the center pixel,

performing the filter calculation for the center pixel using the metric values for the those kernel pixels which were determined to be included in the filter calculation, and

replacing the vector value of the center pixel with a calculated vector value from the filter calculation for the center pixel.

7. The method of claim 6 wherein defining a metric further comprises

determining the simple vector distance between the vector values of the center pixel and a second pixel.

8. The method of claim 6 wherein defining a sliding processing kernel relative to the center pixel further comprises

selecting a kernel shape relative to the center pixel;

setting a kernel size; and

setting a value contrast threshold for the kernel.

9. The method of claim 8 wherein setting a kernel size further comprises

setting a kernel size dynamically based on the intensity of an image and on a magnitude threshold value.

10. The method of claim 9 wherein setting a kernel size dynamically based on the intensity of an image and on a magnitude threshold value further comprises

using the magnitude threshold value to detect that a portion of an image is of low intensity;

and

setting a large kernel size for the low intensity portion of an image.

11. The method of claim 10 wherein using a magnitude threshold value to detect that an image is of low intensity further comprises

setting the magnitude threshold value as the standard deviation of the magnitude of the overall image subtracted from the mean overall magnitude of the image.

12. The method of claim 8 wherein setting a value contrast threshold for the kernel further comprises

using a statistical method to set a value contrast threshold.

13. The method of claim 8 wherein setting a value contrast threshold further comprises

setting a value contrast threshold dynamically through a statistical method comprising

taking the mean average of the vector values of the pixels in the kernel;

finding the standard deviation of the vector values of the pixels in the kernel; and

setting the value contrast threshold to the mean average plus the standard deviation.

14. An automated method for sharpening a digitized, vector-valued, multi-spectral image A, the image comprising a plurality of pixels, the pixels each having vector values comprising a plurality of spectral components, the method comprising the computer-implemented steps of

creating multi-spectral image sharpening software comprising

performing a low-pass filter of image A to obtain a blurred image B1 with noise and signal suppressed,

subtracting the resulting blurred image B1 from the original image A to produce a high frequency band C1 that contains noise and signal,

using vector difference mean filtering on the original image A to produce a filtered image B2 with noise suppressed, such that the vector difference mean filtering filters the image A according to the vector values of image A,

subtracting the filtered image B2 from the original image A to produce a noise band C2 that contains noise with very little signal,

subtracting the noise band C2 from the high frequency band C1 to produce a signal band D that contains the signal, thereby recovering signal that was lost in the low pass filtering step, and

adding the signal band D to the filtered image B2 to further enhance detail in the noise filtered band;

receiving a digital image;

sharpening the digital image using the sharpening software; and

returning a sharpened image.

15. The method of claim 14 wherein the low-pass filter is a spatial average filter.

16. The method of claim 14 wherein the vector values further comprise

a red scalar color component value for each of the plurality of pixels;

a green scalar color component value for each of the plurality of pixels; and

a blue scalar color component value for each of the plurality of pixels.

17. The method of claim 14 wherein the vector values further comprise

a red scalar color amplitude value for each of the plurality of pixels;

a red scalar color phase value for each of the plurality of pixels;

a green scalar color amplitude value for each of the plurality of pixels

a green scalar color phase value for each of the plurality of pixels;

a blue scalar color amplitude value for each of the plurality of pixels; and

a blue scalar color phase value for each of the plurality of pixels.

18. The method of claim 14 wherein the vector values further comprise

a cyan scalar color component value for each of the plurality of pixels;

a magenta scalar color component value for each of the plurality of pixels;

a yellow scalar color component value for each of the plurality of pixels; and

a black scalar color component value for each of the plurality of pixels.

19. The method of claim 14 wherein the vector difference mean filtering further comprises

defining a metric, such that the metric uses the vector values of a first pixel and a second pixel to determine a metric value for the pixel;

and

filtering each of the portion of the plurality of pixels within the image by

designating a center pixel;

defining a sliding processing kernel relative to the center pixel, the kernel comprising kernel pixels which include the center pixel and a plurality of pixels in proximity to the center pixel;

for each pixel associated with the sliding processing kernel

using the metric to compute the metric value for the pixel from the center pixel vector value and the pixel vector value,

comparing the metric value for the pixel to a threshold value for the kernel pixels in order to determine whether to include the pixel value in a filter calculation for the center pixel,

performing the filter calculation for the center pixel using the metric values for the those kernel pixels which were determined to be included in the filter calculation, and

replacing the vector value of the center pixel with a calculated vector value from the filter calculation for the center pixel.

20. The method of claim 19 wherein defining a metric further comprises

determining the simple vector distance between the vector values of the center pixel and a second pixel.

21. The method of claim 19 wherein defming a sliding processing kernel relative to the center pixel further comprises

selecting a kernel shape relative to the center pixel;

setting a kernel size; and

setting a value contrast threshold for the kernel.

22. A system for sharpening digitized, vector-valued, multi-spectral images to suppress noise, the system comprising

a computing environment;

means for receiving a digitized multi-spectral image from a source environment;

sharpening software based on vector values of a plurality of pixels within the image,

the software providing a sharpened image by

performing a low-pass filter of image A to obtain a blurred image B1 with noise and signal suppressed,

subtracting the resulting blurred image B1 from the original image A to produce a high frequency band C1 that contains noise and signal,

using vector difference mean filtering on the original image A to produce a filtered image B2 with noise suppressed, such that the vector difference mean filtering filters the image A according to the vector values of image A,

subtracting the filtered image B2 from the original image A to produce a noise band C2 that contains noise with very little signal,

subtracting the noise band C2 from the high frequency band C1 to produce a signal band D that contains the signal, thereby recovering signal that was lost in the low pass filtering step, and

adding the signal band D to the filtered image B2 to further enhance detail in the noise filtered band; and

a means for transmitting the sharpened image to a target environment.