Blind restoration of images degraded by isotropic blur

• US 8,249,357 B2
• Filed: 10/22/2007
• Issued: 08/21/2012
• Est. Priority Date: 10/23/2006
• Status: Active Grant

• Current Assignee: Ben Gurion University of The Negev
• Original Assignee: Ben Gurion University of The Negev
• Inventors: Yitzhaky, Yitzhak, Shacham, Omri, Haik, Oren
• Primary Examiner(s): Patel, Jayesh A
• Application Number:
  US12/446,790
• Publication Number:
  US 20100080487A1
• Time in Patent Office: 1,765 Days
• Field of Search: 382194-199, 382/242, 382/266, 382/275
• US Class Current: 382/199
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/13 Edge detection

Abstract

A system and a method for automatic restoration of isotropic degradations of a digital image, based on receiving a blurred image by an image capture assembly, automatically finding proper step edge, calculating the PSF from the step edge, and restoring the blurred image by means of a processor, and with the option to display the resorted image by means of an output assembly.

Claims

1. A system for automatic restoration of isotropic degradations of a digital image, the system comprising:

(a) an image capture assembly; and

(b) a processor, which is coupled to process automatically said digital image so as to derive a restoration of said digital image;

wherein said processor includes;

(i) a memory, wherein said memory includes software instructions, wherein said software instructions executing a preprogrammed logic algorithm including the stages of;

A. receiving a blurred image comprising a plurality of pixels;

B. finding automatically a proper step edge by decomposing said pixels into a plurality of pixel chains, weighting said pixel chains according to straightness and homogeneity, and selecting a pixel chain as said step edge according to said weighting;

C. calculating a PSF from the step edge; and

D. restoring said blurred image.

2. The system of claim 1, wherein said memory does not store previous knowledge about a point spread function of a disturbance.

3. The system of claim 1, wherein said algorithm includes no iterative stage.

4. The system of claim 1, wherein said algorithm calculates said PSF from information that can be found in said image.

5. The system of claim 1 further comprising:

(c) an output assembly, wherein said output assembly can receive a restored image from said processor.

6. The system of claim 1, wherein said image capture assembly is a digitizing equipment.

7. The system of claim 1, wherein said image capture assembly is a CCD camera.

8. The system of claim 1, wherein said image capture assembly is an infrared camera.

9. The system of claim 5, wherein said output assembly is a display screen.

10. A method for automatic restoration of isotropic degradations of a digital image, the method comprising the stages of:

receiving a blurred image;

automatically finding a proper step edge;

wherein said stage of automatically finding said proper step edge includes;

finding edges in said blurred image;

erasing edge pixels having more than two neighbors;

recognizing an angle among neighbors of each edge pixel;

finding straight and long edges according to said angles;

finding edge pixels with homogenous areas in both sides of the said edges, and with high contrast; and

selecting the most appropriate edge;

calculating a PSF from the step edge; and

restoring said blurred image as a resorted image according to said PSF.

11. The method for automatic restoration of isotropic degradations of a digital image of claim 10 further comprising the stage of:

displaying said restored image.

12. The method of claim 10 wherein said stage of calculating the PSF from the step edge includes;

selecting a few vectors of the 2-D edge;

constructing ESF; and

using said vectors for calculating a LSF.

13. A method for automatic restoration of isotropic degradations of a digital image, the method comprising:

receiving a blurred image;

finding edges in said blurred image, said edges having a plurality of edge pixels;

erasing edge pixels having more than two neighbors;

recognizing an angle among neighbors of each edge pixel;

finding straight and long edges according to said angles;

finding edge pixels with homogenous areas in both sides of the said edges, and with high contrast;

selecting the most appropriate edge as a selected step edge;

selecting a few vectors of said selected step edge;

constructing an ESF;

using said vectors for calculating a LSF;

restoring said blurred image as a resorted image; and

displaying said restored image.