Method and system for implementing fuzzy image processing of image data

• US 5,778,156 A
• Filed: 05/08/1996
• Issued: 07/07/1998
• Est. Priority Date: 05/08/1996
• Status: Expired due to Term

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Schweid, Stuart A., Williams, Leon C., Beikirch, Thomas R.
• Primary Examiner(s): Downs, Robert W.
• Application Number:
  US08/646,608
• Time in Patent Office: 790 Days
• Field of Search: 382/173, 382/176, 382/260, 382/261, 382/224, 395/61
• US Class Current: 706/52
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30176 Document
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling

Abstract

A system and method electronically image process a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes. This process uses classification to determine a membership value for the pixel for each image classes and generates an effect tag for the pixel based on the fuzzy classification determination. The pixel is image processed based on the membership vector of the pixel. The image processing may include screening and filtering. The screening process screens the pixel by generating a screen value according to a position of the pixel in the set of digital image data; generating a screen amplitude weighting value based on the values in the membership vector for the pixel; multiplying the screen value and the screen amplitude weighting value to produce a modified screen value; and adding the modified screen value to the pixel of image data. The filtering process filters the pixel by lowpass filtering the pixel; highpass filtering the pixel; non-filtering the pixel; multiplying each filtered pixel by a gain factor based on the values in the membership vector associated with the pixel; and adding the products to produce a filtered pixel of image data.

Claims

1. A method for electronically image processing a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising the steps of:

(a) receiving a set of digital image data including the pixel;

(b) determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, the sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

(c) generating a classification vector for the pixel based on the determination in said step (b); and

(d) image processing the pixel based on membership values in the classification vector of the pixel.

2. The method as claimed in claim 1, wherein said step (d) includes the substep of two-dimensionally filtering the pixel of image data.

3. The method as claimed in claim 2, wherein the two-dimensionally filtering substep includes the substeps of:

(d1) applying a plurailty of filters in parallel to the pixel of image data;

(d2) weighting the output from each filter based on a set of weighting coefficients; and

(d3) adding the weighted outputs to produce a filtered pixel of image data.

4. The method as claimed in claim 3, wherein each parallel filter performs the substeps of:

(d4) applying a plurality of first dimensional filters to the pixel of image data, the filtering being based on a set of first dimensional filter coefficients; and

(d5) applying a plurality of second dimensional filters to the pixel of image data filtered by said substep (d4), the filtering being based on a set of second dimensional filter coefficients.

5. The method as claimed in claim 1, wherein said step (d) includes the substep of screening the pixel of image data.

6. The method as claimed in claim 1, wherein the set of non-mutually exclusive image classes include text and contone.

7. The method as claimed in claim 1, wherein the set of non-mutually exclusive image classes include text, contone, and halftone.

8. The method as claimed in claim 1, wherein the set of non-mutually exclusive image classes include edge, black, white, pictorial, low frequency halftone, mid frequency halftone, and high frequency halftone.

9. The method as claimed in claim 1, wherein said step (b) includes the substeps of:

(b1) calculating a summation of absolute values of Laplacians of a window of pixels around the pixel to be classified;

(b2) determining a grey level range within the window of pixels;

(b3) determining a two-dimensional frequency around the pixel be classified;

(b4) determining a percentage of pixels in the window having a grey value less than a predetermined black threshold; and

(b5) determining a percentage of pixels in the window having a grey value greater than a predetermined white threshold.

10. The method as claimed in claim 9, wherein said step (b) further includes the substeps of:

(b6) determining a conditional value of the pixel with respect to a condition of the summation of said substep (b1) being greater than a first predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (b2) being greater than a second predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (b3) being greater than a third predetermined value;

(b7) establishing a conditional value for the pixel with respect to a halftone class by determining which conditional value determined in said substep (b6) is a minimum value and assigning the minimum value as the conditional value;

(b8) determining a conditional value of the pixel with respect to a condition of the summation of said substep (b1) being greater than a fourth predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (b2) being greater than a fifth predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (b3) being greater than the third predetermined value;

(b9) establishing a membership value for the pixel with respect to an edge class by determining which conditional value determined in said substep (b8) is a minimum value and assigning the minimum value as the membership value;

(b10) determining a conditional value of the pixel with respect to a condition of the summation of said substep (b1) being less than the first predetermined value and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (b3) being less than the third predetermined value; and

(b11) establishing a conditional value for the pixel with respect to a contone class by determining which conditional value determined in said substep (b10) is a minimum value and assigning the minimum value as the conditional value.

11. The method as claimed in claim 10, wherein said step (b) further includes the substeps of:

(b12) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (b4) being greater than a sixth predetermined value;

(b13) establishing a membership value for the pixel with respect to a black class by determining which conditional value determined in said substeps (b11) and (b12) is a minimum value and assigning the minimum value as the membership value;

(b14) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (b5) being greater than a seventh predetermined value;

(b15) establishing a membership value for the pixel with respect to a white class by determining which conditional value determined in said substeps (b11) and (b14) is a minimum value and assigning the minimum value as the membership value;

(b16) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (b4) being less than the sixth predetermined value and determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (b5) being less than the seventh predetermined value; and

(b17) establishing a membership value for the pixel with respect to a pictorial class by determining which conditional value determined in said substeps (b11) and (b16) is a minimum value and assigning the minimum value as the membership value.

12. The method as claimed in claim 11, wherein said step (b) further includes the substeps of:

(b18) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (b3) being less than an eighth predetermined value;

(b19) establishing a membership value for the pixel with respect to a low frequency halftone class by determining which conditional value determined in said substeps (b7) and (b18) is a minimum value and assigning the minimum value as the membership value;

(b20) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (b3) being greater than the eighth predetermined value and determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (b3) being less than a ninth predetermined value;

(b21) establishing a membership value for the pixel with respect to a mid frequency halftone class by determining which conditional value determined in said substeps (b7) and (b20) is a minimum value and assigning the minimum value as the membership value;

(b22) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (b3) being greater than the ninth predetermined value; and

(b23) establishing a membership value for the pixel with respect to a high frequency halftone class by determining which conditional value determined in said substeps (b7) and (b22) is a minimum value and assigning the minimum value as the membership value.

13. The method as claimed in claim 12, wherein said step (b) further includes the substep of:

(b24) establishing a membership value for the pixel with respect to the other class by determining which membership value determined in said substeps (b9), (b13), (b15), (b17), (b19), (b21), and (b23) is a maximum value and assigning a value equal to one minus the maximum value as the membership value for the other.

14. A system for electronically image processing a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising:

fuzzy classifying means for determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, said sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

effect means for generating a classification vector for the pixel based on the determination by said fuzzy classifying means; and

processing means for image processing the pixel based membership values in the classification vector of the pixel.

15. The system as claimed in claim 14, wherein said processing means includes a two-dimensional filter.

16. The system as claimed in claim 15, wherein said two-dimensional filter includes:

a plurality of filters to filter the image data in parallel;

a set of multipliers to weight the output from each filter based on a set of weighting coefficients; and

an adder to add the weighted outputs to produce a filtered pixel of image data.

17. The system as claimed in claim 16, wherein each parallel filter includes:

a plurality of first dimensional filters to filter the pixel of image data, the filtering being based on a set of first dimensional filter coefficients; and

a plurality of second dimensional filters to filter the pixel of image data filtered by said first dimensional filters, the filtering being based on a set of second dimensional filter coefficients.

18. The system as claimed in claim 14, wherein said processing means includes a screening circuit to screen the pixel of image data.

19. A method for electronically screening a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising the steps of:

(a) determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, the sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

(b) generating a classification vector for the pixel based on the determination in said step (a); and

(c) screening the pixel based membership values in the classification vector associated with the pixel.

20. The method as claimed in claim 19, wherein said step (c) includes the substeps of:

(c1) generating a screen value according to a position of the pixel in the set of digital image data;

(c2) generating a screen amplitude weighting value based on the membership values in the classification vector for the pixel;

(c3) multiplying the screen value and the screen amplitude weighting value to produce a modified screen value; and

(c4) adding the modified screen value to the pixel of image data.

21. The method as claimed in claim 20, wherein said step (a) includes the substeps of:

(a1) calculating a summation of absolute values of Laplacians of a window of pixels around the pixel to be classified;

(a2) determining a grey level range within the window of pixels;

(a3) determining a two-dimensional frequency around the pixel be classified;

(a4) determining a percentage of pixels in the window having a grey value less than a predetermined black threshold; and

(a5) determining a percentage of pixels in the window having a grey is value greater than a predetermined white threshold.

22. The method as claimed in claim 21, wherein said step (a) further includes the substeps of:

(a6) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being greater than a first predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (a2) being greater than a second predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being greater than a third predetermined value;

(a7) establishing a conditional value for the pixel with respect to a halftone class by determining which conditional value determined in said substep (a6) is a minimum value and assigning the minimum value as the conditional value;

(a8) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being greater than a fourth predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (a2) being greater than a fifth predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being greater than the third predetermined value;

(a9) establishing a membership value for the pixel with respect to an edge class by determining which conditional value determined in said substep (a8) is a minimum value and assigning the minimum value as the membership value;

(a10) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being less than the first predetermined value and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being less than the third predetermined value; and

(a11) establishing a conditional value for the pixel with respect to a contone class by determining which conditional value determined in said substep (a10) is a minimum value and assigning the minimum value as the conditional value.

23. The method as claimed in claim 22, wherein said step (a) further includes the substeps of:

(a12) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a4) being greater than a sixth predetermined value;

(a13) establishing a membership value for the pixel with respect to a black class by determining which conditional value determined in said substeps (a11) and (a12) is a minimum value and assigning the minimum value as the membership value;

(a14) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a5) being greater than a seventh predetermined value;

(a15) establishing a membership value for the pixel with respect to a white class by determining which conditional value determined in said substeps (a11) and (a14) is a minimum value and assigning the minimum value as the membership value;

(a16) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a4) being less than the sixth predetermined value and determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a5) being less than the seventh predetermined value; and

(a17) establishing a membership value for the pixel with respect to a pictorial class by determining which conditional value determined in said substeps (a11) and (a16) is a minimum value and assigning the minimum value as the membership value.

24. The method as claimed in claim 23, wherein said step (a) further includes the substeps of:

(a18) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being less than an eighth predetermined value;

(a19) establishing a membership value for the pixel with respect to a low frequency halftone class by determining which conditional value determined in said substeps (a7) and (a18) is a minimum value and assigning the minimum value as the membership value;

(a20) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being greater than the eighth predetermined value and determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being less than a ninth predetermined value;

(a21) establishing a membership value for the pixel with respect to a mid frequency halftone class by determining which conditional value determined in said substeps (a7) and (a20) is a minimum value and assigning the minimum value as the membership value;

(a22) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being greater than the ninth predetermined value; and

(a23) establishing a membership value for the pixel with respect to a high frequency halftone class by determining which conditional value determined in said substeps (a7) and (a22) is a minimum value and assigning the minimum value as the membership value.

25. The method as claimed in claim 24, wherein said step (a) further includes the substep of:

(a24) establishing a membership value for the pixel with respect to the other class by determining which membership value determined in said substeps (a9), (a13), (a15), (a17), (a19), (a21), and (a23) is a maximum value and assigning a value equal to one minus the maximum value as the membership value for the other.

26. A method for electronically filtering a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising the steps of:

(a) determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, the sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

(b) generating a classification vector for the pixel based on the determination in said step (a); and

(c) two-dimensionally filtering the pixel based on membership values in the classification vector associated with the pixel.

27. The method as claimed in claim 26, wherein said step (c) includes the substeps of:

(c1) lowpass filtering the pixel;

(c2) highpass filtering the pixel;

(c3) non-filtering the pixel;

(c4) multiplying the lowpass filtered pixel by a gain factor based on the membership values in the classification vector associated with the pixel;

(c5) multiplying the highpass filtered pixel by a gain factor based on the membership values in the classification vector associated with the pixel;

(c6) multiplying the non-filtered pixel by a gain factor based on the membership values in the classification vector associated with the pixel; and

(c7) adding the products of said steps (c4), (c5), and (c6) to produce a filtered pixel of image data.

28. The method as claimed in claim 27, wherein said step (a) includes the substeps of:

(a1) calculating a summation of absolute values of Laplacians of a window of pixels around the pixel to be classified;

(a2) determining a grey level range within the window of pixels;

(a3) determining a two-dimensional frequency around the pixel be classified;

(a4) determining a percentage of pixels in the window having a grey value less than a predetermined black threshold; and

(a5) determining a percentage of pixels in the window having a grey value greater than a predetermined white threshold.

29. The method as claimed in claim 28, wherein said step (a) further includes the substeps of:

(a6) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being greater than a first predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (a2) being greater than a second predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being greater than a third predetermined value;

(a7) establishing a conditional value for the pixel with respect to a halftone class by determining which conditional value determined in said substep (a6) is a minimum value and assigning the minimum value as the conditional value;

(a8) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being greater than a fourth predetermined value, determining a conditional value of the pixel with respect to a condition of the determined range of said substep (a2) being greater than a fifth predetermined value, and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being greater than the third predetermined value;

(a9) establishing a membership value for the pixel with respect to an edge class by determining which conditional value determined in said substep (a8) is a minimum value and assigning the minimum value as the membership value;

(a10) determining a conditional value of the pixel with respect to a condition of the summation of said substep (a1) being less than the first predetermined value and determining a conditional value of the pixel with respect to a condition of the determined frequency of said substep (a3) being less than the third predetermined value; and

(a11) establishing a conditional value for the pixel with respect to a contone class by determining which conditional value determined in said substep (a10) is a minimum value and assigning the minimum value as the conditional value.

30. The method as claimed in claim 29, wherein said step (a) further includes the substeps of:

(a12) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a4) being greater than a sixth predetermined value;

(a13) establishing a membership value for the pixel with respect to a black class by determining which conditional value determined in said substeps (a11) and (a12) is a minimum value and assigning the minimum value as the membership value;

(a14) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a5) being greater than a seventh predetermined value;

(a15) establishing a membership value for the pixel with respect to a white class by determining which conditional value determined in said substeps (a11) and (a14)is a minimum value and assigning the minimum value as the membership value;

(a16) determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a4) being less than the sixth predetermined value and determining a conditional value of the pixel with respect to a condition of the percentage determined in said substep (a5) being less than the seventh predetermined value; and

(a17) establishing a membership value for the pixel with respect to a pictorial class by determining which conditional value determined in said substeps (a11) and (a16) is a minimum value and assigning the minimum value as the membership value.

31. The method as claimed in claim 30, wherein said step (a) further includes the substeps of:

(a18) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being less than an eighth predetermined value;

(a19) establishing a membership value for the pixel with respect to a low frequency halftone class by determining which conditional value determined in said substeps (a7) and (a18) is a minimum value and assigning the minimum value as the membership value;

(a20) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being greater than the eighth predetermined value and determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being less than a ninth predetermined value;

(a21) establishing a membership value for the pixel with respect to a mid frequency halftone class by determining which conditional value determined in said substeps (a7) and (a20) is a minimum value and assigning the minimum value as the membership value;

(a22) determining a conditional value of the pixel with respect to a condition of the frequency determined in said substep (a3) being greater than the ninth predetermined value; and

(a23) establishing a membership value for the pixel with respect to a high frequency halftone class by determining which conditional value determined in said substeps (a7) and (a22) is a minimum value and assigning the minimum value as the membership value.

32. The method as claimed in claim 31, wherein said step (a) further includes the substep of:

(a24) establishing a membership value for the pixel with respect to the other class by determining which membership value determined in said substeps (a9), (a13), (a15), (a17), (a19), (a21), and (a23) is a maximum value and assigning a value equal to one minus the maximum value as the membership value for the other.

33. A system for electronically screening a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising:

fuzzy classifying means for determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, the sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

effect means for generating a classification vector for the pixel based on the determination by said fuzzy classifying means; and

screening means screening the pixel based on the membership values in the classification vector associated with the pixel.

34. The system as claimed in claim 33, wherein said screening means includes:

means for generating a screen value according to a position of the pixel in the set of digital image data;

means for generating a screen amplitude weighting value based on the membership values in the classification vector for the pixel;

means for multiplying the screen value and the screen amplitude weighting value to produce a modified screen value; and

means for adding the modified screen value to the pixel of image data.

35. A system for electronically filtering a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes and an other class, comprising:

fuzzy classifying means for determining a membership value for the pixel for each image class and a membership value for the pixel for the other class, each image class being defined by a set of heuristic rules, the sets being non-mutually exclusive, the membership value for an image class being independent of the other membership values for the image classes, the membership value for the other class being dependent on the membership values of the image classes;

effect means for generating a classification vector for the pixel based on the determination by said fuzzy classifying means; and

filter means for two-dimensionally filtering the pixel based on the membership values in the classification vector associated with the pixel.

36. The method as claimed in claim 35, wherein said filter means includes:

a lowpass filter;

a highpass filter;

a bypass filter;

a first multiplier to multiply the lowpass filtered pixel by a gain factor based on the membership values in the classification vector associated with the pixel;

a second multiplier to multiply the highpass filtered pixel by a gain factor based on the membership values in the classification vector associated with the pixel;

a third multiplier to multiply the bypass filtered pixel by a gain factor based the membership values in the classification vector associated with the pixel; and

an adder to add the products of said first, second, and third multipliers to produce a filtered pixel of image data.