Method and apparatus for enhancing and thresholding images

• US 6,072,907 A
• Filed: 05/19/1998
• Issued: 06/06/2000
• Est. Priority Date: 05/28/1997
• Status: Expired due to Term

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Zappala, Anthony, Taylor, Michael J.
• Primary Examiner(s): Mehta, Bhavesh
• Application Number:
  US09/081,269
• Time in Patent Office: 749 Days
• Field of Search: 382/237, 382/260, 382/263, 382/270, 382/272, 382/274, 382/275, 382/279, 382/299-300, 358/428, 358/429, 358/455, 358/461, 358/465, 358/466, 358/525
• US Class Current: 382/237
• CPC Class Codes: G06T 2207/10008 from scanner, fax or copier
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/30176 Document
• CPC Class Codes: G06T 5/92 based on global image properties
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/162 Quantising the image signal

Abstract

An image processing method and system for generating enhanced binary image data from greyscale input image data. The method includes the steps of (a) receiving first image data, the first image data being greyscale image data defining an input image, (b) performing a high frequency boost operation on the first image data to produce second image data, (c) performing a linear interpolation operation on the second image data to produce third image data, the third image data having a resolution higher than the resolution of the second image data, (d) performing a contrast enhancement operation on the third image data to produce fourth image data, and (e) thresholding the fourth image data to produce fifth image data, the fifth image data being binary image data defining an output image. The techniques find application, for example, in over-the-desk scanning of documents, and in video-conferencing.

Claims

1. A method for converting a low resolution greyscale image to a high resolution binary image with an image processing system, comprising the steps of:

(a) receiving first image data;

the first image data being greyscale image data defining an input image;

(b) performing a high frequency boost operation on the first image data to produce second image data;

(c) performing a linear interpolation operation on the second image data to produce third image data;

the third image data having a resolution higher than the resolution of the second image data;

(d) performing a contrast enhancement operation on the third image data to produce fourth image data; and

(e) thresholding the fourth image data to produce fifth image data;

the fifth image data being binary image data defining an output image.

2. The method according to claim 1, wherein said step (d) further comprises the steps of:

(d1) computing high gradient image data (g(x,y)) using the third image data (f(x,y)).

3. The method according to claim 2, wherein said step (d1) comprises applying a Sobel operator to the third image data (f(x,y)) to produce the high gradient image data (g(x,y)).

4. The method according to claim 2, wherein said step (d) further comprises the step of:

(d2) computing sum image data (s(x,y)) from partial sums of the high gradient image data (g(x,y)).

5. The method according to claim 4, wherein said step (d2) computes the sum image data (s(x,y)) with the following equation:

##EQU2##

6. The method according to claim 4, wherein said step (d) further comprises performing for each pixel datum of the third image data (f(x,y)) the step of:

(d3) applying a contrast enhancement function (e(m)) to the third image data;

the contrast enhancement function being dependent on a local average (m(x,y)) of pixel data.

7. The method according to claim 6, wherein step (d3) further comprises the steps of:

(d3i) computing a local average (m(x,y)) of pixel data;

(d3ii) forming the contrast enhancing function (e(m)) based on the local average; and

(d3iii) applying the contrast enhancement function (e(m)) to the third image data.

8. The method according to claim 7, wherein said step (d3i) computes the local average m_i for a pixel f_i using the following equation:

space="preserve" listing-type="equation">m.sub.i =S.sub.i /N.sub.i,

where

S_i is the sum of high-gradient image data in an N by N neighbourhood about f_i, and

N_i is the number of high gradient pixels in the N by N neighbourhood about f_i.

9. The method according to claim 8, wherein said step (d3i) computes the sum of high gradient pixels S_i in an N by N neighbourhood about f_i using the following equation:

space="preserve" listing-type="equation">S.sub.i =S(c,d)-S(a,d)-S(c,b)+S(a,b),

where, S(c,d), S(a,d), S(c,b), and S(a,b) are elements of the sum image data s(x,y).

10. An image processing system for converting a low resolution greyscale image to a high resolution binary image, comprising:

a memory for storing the low resolution greyscale image, the high resolution binary image, and image processing instructions; and

a processor coupled to the memory for executing the image processing instructions;

the processor in executing the image processing instructions;

(a) receiving first image data;

the first image data being greyscale image data defining an input image;

(b) performing a high frequency boost operation on the first image data to produce second image data;

(c) performing a linear interpolation operation on the second image data to produce third image data;

the third image data having a resolution higher than the resolution of the second image data;

(d) performing a contrast enhancement operation on the third image data to produce fourth image data; and

(e) thresholding the fourth image data to produce fifth image data, the fifth image data being binary image data defining an output image.

11. The image processing system according to claim 10, further comprising an interactive desktop system including:

a work surface;

a display device for displaying images in a display area; and

an image capture device for capturing images present on the work surface;

wherein said processor is coupled to said display device and said image capture device for displaying the output image in at least a portion of said display area.

12. The image processing system according to claim 10, further comprising a communications link for transmitting the fifth image data to a second processor.

13. The image processing system according to claim 10, further comprising image processing instructions for

(d1) computing high gradient image data (g(x,y)) using the third image data (f(x,y));

(d2) computing sum image data (s(x,y)) from partial sums of the high gradient image data (g(x,y)); and

(d3) applying a contrast enhancement function (e(m)) to the third image data;

the contrast enhancement function being dependent on a local average (m(x,y)) of pixel data.

14. The image processing system according to claim 13, wherein said instruction (d1) further comprises an instruction for applying a Sobel operator to the third image data (f(x,y)) to produce the high gradient image data (g(x,y)).

15. The image processing system according to claim 13, wherein said instruction (d2) further comprises an instruction for computing the sum image data (s(x,y)) with the following equation:

##EQU3##

16. The image processing system according to claim 13, wherein said instruction (d) further comprises instructions for:

(d3i) computing a local average (m(x,y)) of pixel data;

(d3ii) forming the contrast enhancing function (e(m)) based on the local average; and

(d3iii) applying the contrast enhancement function (e(m)) to the third image data.

17. The image processing system according to claim 16, wherein said instruction (d3i) computes the local average m_i for a pixel f_i using the following equation:

space="preserve" listing-type="equation">m.sub.i =S.sub.i /N.sub.i,

where

S_i is the sum of high-gradient image data in an N by N neighbourhood about f_i, and

N_i is the number of high gradient pixels in the N by N neighbourhood about f_i.

18. The image processing system according to claim 17, wherein said instruction (d3i) computes the sum of high gradient pixels S_i in an N by N neighbourhood about f_i using the following equation:

space="preserve" listing-type="equation">S.sub.i =S(c,d)-S(a,d)-S(c,b)+S(a,b),

where, S(c,d), S(a,d), S(c,b), and S(a,b) are elements of the sum image data s(x,y).