Reduction of blur in multi-channel images

• US 20060093234A1
• Filed: 11/04/2004
• Published: 05/04/2006
• Est. Priority Date: 11/04/2004
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Silverstein, D. Amnon
• Application Number:
  US10/982,459
• Publication Number:
  US 20060093234A1
• Time in Patent Office: Days
• US Class Current: 382/255
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20032 Median filtering
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 5/00 Image enhancement or restoration
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/003 Deblurring; Sharpening vibration or motion blur correction for cameras comprising an electronic image sensor H04N23/682
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening

Abstract

A color digital image is processed to reduce blur. First and second color channels of a high frequency feature (e.g., an edge) in the image are compared to derive information that is missing from the second channel due to the blur. The information is used to adjust the feature in the second channel so that sharpeness of the feature is similar in both the first and second channels. As a first example, the processing may be used to correct chromatic aberration in an image captured by a digital camera. As a second example, the processing may be used to reduce blur in images created during film restoration.

Claims

1. A method of reducing blur in a multi-channel digital image, the method comprising:

comparing first and second color channels of a high frequency feature in the image to derive information that is missing from the second channel due to the blur; and

using the information to adjust the feature in the second channel so that sharpeness of the feature is similar in both the first and second channels.

2. The method of claim 1, wherein the image includes a third color channel, and wherein the method further comprises:

comparing the first and third channels of the high frequency feature to derive additional information that is missing from the third channel due to the blur; and

using the additional information to adjust the feature in the third channel so that sharpeness of the feature is similar in the first, second and third channels.

3. The method of claim 1, wherein a difference is computed between the feature in the first color channel and the feature in the second channel;

wherein the difference is high-pass filtered, and wherein the filtered difference is combined with the feature in the second channel.

4. The method of claim 3, wherein the first channel is scaled to have the approximate levels of the second channel prior to computing the difference.

5. The method of claim 3, further comprising using an iterative back-projection to further adjust the sharpness in the second channel.

6. The method of claim 1, wherein comparing the first and second color channels includes computing a blur estimate between the first and second channels;

and wherein adjusting the feature in the second channel includes using the first channel to produce a scaled approximation of the second channel; and

applying the blur estimate to the scaled approximation to determine a sharpened replacement for the second channel.

7. The method of claim 6, wherein B′

=N_b(B)+(Bˆ

−

L_b(Bˆ

)), where B represents the second channel, Bˆ

represents the approximation of the second channel, B′

represents the sharpened replacement for the second channel, L_b is a filter representing the blur estimate, and N_b is a low pass filter that reduces noise in the second channel.

8. The method of claim 6, further comprising using the sharpened replacement and the first channel to compute a scaled approximation of a third channel, the third channel being blurrier than the second channel;

computing a second blur estimate between the first and third channels; and

applying the second blur estimate to the scaled approximation of the third channel to determine a sharpened replacement for the third channel.

9. The method of claim 8, wherein B′

=N_b(B)+(Bˆ

−

L_b(Bˆ

)) and C′

=N_c(C)+(Cˆ

−

L_c(Cˆ

)), where B and C represent the second and third channels, Bˆ and

Cˆ

represent the approximations of the second and third channels, B′ and

C′

represent the sharpened replacements for the second and third channels, L_b and L_c are filters that represent the blur estimates, and N_b and N_c are low pass filters that reduce noise in the second and third channels.

10. The method of claim 1, further comprising identifying the first channel as being sharper than the second channel.

11. The method of claim 1, further comprising ensuring that the feature has direct spatial correspondence in the first and second channels.

12. The method of claim 1, further comprising removing point-like noise from the image prior to comparing the first and second channels.

13. The method of claim 1, wherein blur reduction is performed globally.

14. The method of claim 1, wherein the blur reduction is performed one pixel at a time.

15. The method of claim 14, wherein the blur reduction of a pixel includes:

high pass filtering a local neighborhood of the pixel;

computing a difference of high pass filtered edges for each channel that is blurred; and

adding the difference values to the corresponding pixel values in the blurred channel.

16. The method of claim 15, further comprising identifying the first channel prior to computing the difference for each channel, whereby the color of the first channel can change from pixel to pixel.

17. The method of claim 15, further comprising using an iterative back-projection to further adjust the sharpness.

18. The method of claim 1, wherein the digital image is captured by an optical system;

and wherein the digital image has chromatic aberrations cause by the optical system.

19. The method of claim 1, wherein the digital image is taken from a film having separate color channels.

20. A processor for performing the method of claim 1.

21. An article comprising memory encoded with data for causing a processor to process a digital image according to claim 1.

22. An article comprising memory encoded with the digital image processed according to claim 1.

23. A system comprising a sensor, optics for focusing an image onto the sensor, and a processor for processing an output of the sensor according to the method of claim 1.

24. The system of claim 23, wherein the optics are positioned so that the red channel is in focus for distant objects.

25. A method of restoring film having layers of different colors, the method comprising capturing digital images of the film;

and reducing blur in the images as recited in claim 1.

26. The method of claim 25, wherein the film includes multiple strips, each strip corresponding to a color channel;

and wherein capturing the film includes scanning frames of each strip and registering the frames.

27. A method for an image capture device, the method comprising capturing an image;

pre-processing the image; and

reducing blur in the pre-processed image, the blur reduction including;

comparing a sharpest channel to derive high frequency information that is missing from blurred channels due to the blur; and

using the information to adjust the blurred channels so that sharpeness of the blurred channels is similar to the sharpness of the sharpest channel.

28. A method of restoring film, the method comprising projecting frames of the film onto a scanner;

and for each frame

computing a blur estimate that, when applied to a first channel, approximates blur in a second channel;

using the first channel to produce a scaled approximation of the second channel; and

applying the blur estimate to the scaled approximation to determine a sharpened replacement for the second channel.

29. The method of claim 28, wherein B′

=N_b(B)+(Bˆ

−

L_b(Bˆ

)), where B represents the second channel, Bˆ

represents the approximation of the second channel, B′

represents the sharpened replacement for the second channel, L_b is a filter representing the blur estimate, and N_b is a low pass filter that reduces noise in the second channel.

30. The method of claim 29, wherein a linear regression is used to compute parameters a and b, where Bˆ

=A×

a+b≅

B.

31. The method of claim 29, further comprising for each frame:

using the sharpened replacement and the first channel to compute a scaled approximation of a third channel, the third channel being blurrier than the second channel;

computing a second blur estimate that, when applied to the first channel, approximates blur in the third channel; and

applying the second blur estimate to the scaled approximation of the third channel to determine a sharpened replacement for the third channel.

32. The method of claim 31, wherein B′

=N_b(B)+(Bˆ

−

L_b(Bˆ

)) and C′

=N_c(C)+(Cˆ

−

L_c(Cˆ

)), where B and C represent the second and third channels, Bˆ and

Cˆ

represent the approximations of the second and third channels, B′ and

C′

represent the sharpened replacements for the second and third channels, L_b and L_c are filters that represent the blur estimates, and N_b and N_c are low pass filters that reduce noise in the second and third channels.

33. The method of claim 32, where linear regression is used to compute parameters a, b, c, d and e, where Bˆ

=A×

a+b≅

B and Cˆ

=B′

×

c+A×

d+e≅

C.

34. The method of claim 28, wherein blur reduction is performed globally.

35. Apparatus comprising a processor for reducing blur in a multi-channel digital image, the blur reduction including

comparing first and second color channels of a high frequency feature in the image to derive information that is missing from the second channel due to the blur;

and

using the information to adjust the feature in the second channel so that sharpeness of the feature is similar in both the first and second channels.

36. The apparatus of claim 35, further comprising a sensor, the processor for processing an output of the sensor.

37. The apparatus of claim 36, further comprising optics for focusing images onto the sensor, wherein the optics are positioned so that one of the channels is in focus for objects in the images.

38. The apparatus of claim 36, wherein a scanner includes the sensor, the scanner for scanning film.

39. The apparatus of claim 38, wherein the film includes multiple strips, each strip corresponding to a color channel;

and wherein processor registers frames of the strips, uses one of the strips as having the least blur, and reduces blur in the other strips.

40. An article for a processor comprising memory encoded with data for causing the processor to reduce blur in a multi-channel digital image, the blur reduction including:

comparing first and second color channels of a high frequency feature in the image to derive information that is missing from the second channel due to the blur; and

using the information to adjust the feature in the second channel so that sharpeness of the feature is similar in both the first and second channels.