ADAPTIVE NOISE FILTER

• US 20160267633A1
• Filed: 03/21/2016
• Published: 09/15/2016
• Est. Priority Date: 07/26/2013
• Status: Active Grant

• Current Assignee: Li-Cor Biotech LLC
• Original Assignee: Li & S Company Limited (Yesco Holdings Co., Ltd.)
• Inventors: Humphrey, Patrick G.
• Granted Patent: US 9,747,676 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/20028 Bilateral filtering
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20182 Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/11 Region-based segmentation

Abstract

A method for applying a filter to data to improve data quality and/or reduce file size. In one example, a region of interest of an image is identified. A histogram is generated of pixel intensity values in the region of interest. The histogram is iteratively updated to focus (zoom) in on the highest peak in the histogram. A Gaussian curve is fitted to the updated histogram. A bilateral filter is applied to the images, where parameters of the bilateral filter are based on the parameters of the Gaussian curve.

Claims

1-6. -6. (canceled)

7. A method for selecting a representative region of interest for an input image, the method comprising:

receiving an input image;

dividing the input image into a plurality of regions;

identifying one or more of the plurality of regions on which to perform a local analysis;

performing the local analysis on each of the one or more identified regions; and

selecting one of the one or more identified regions as a representative region based on results of the local analysis.

8. A method according to claim 7, wherein the dividing the input image into the plurality of regions comprises superimposing a grid onto the input image.

9. A method according to claim 8, wherein the one or more identified regions comprises a plurality of regions proximal to a center of the grid.

10. A method according to claim 8, wherein the one or more identified regions comprises a plurality of regions proximal to outside edges of the grid.

11. A method according to claim 7, wherein the performing the local analysis on a region comprises fitting a Gaussian curve to a histogram of pixel intensity associated with the region.

12. A method according to claim 7, wherein the selecting one of the one or more identified regions as the representative region comprises:

for each of the one or more identified regions;

fitting a Gaussian curve to a histogram of pixel intensity values of the region,

determining whether a first percentage of all of the pixel intensity values in the region is accounted for in the histogram,

determining whether a second percentage of all of the pixel intensity values in the region is below or within the Gaussian curve in the histogram,

determining whether a third percentage of all of the pixel intensity values in the region is outside the Gaussian curve in the histogram;

assigning a quality score to each of the one or more identified regions; and

selecting one region as the representative region based on the quality scores.

13. A method according to claim 12, wherein the selecting one of the one or more identified regions as the representative region comprises calculating an intensity match ratio or a frequency match ratio.

14-16. -16. (canceled)

17. A non-transitory computer readable medium storing code, which when executed by one or more processors cause the one or more processors to implement a method of selecting a representative region of interest for an input image, the code including instructions to:

receive an input image;

divide the input image into a plurality of regions;

identify one or more of the plurality of regions on which to perform a local analysis;

perform the local analysis on each of the one or more identified regions; and

select one of the one or more identified regions as a representative region based on results of the local analysis.