Adaptive noise filter
AssigneeLI-COR, Inc.
InventorPatrick G. HUMPHREY
Life of this patent
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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.
Claims16
A processor-implemented method for applying a filter to an image, the method comprising:
- selecting a first region of the image;
- generating an initial histogram of pixel intensity values in the first region, wherein the histogram includes N bins;
- identifying a first bin as a bin having a greatest count of pixel intensity values;
- identifying a left bin as a closest bin to the left of the first bin in the histogram that has a count that is less than a first count percentage of the greatest count;
- identifying a right bin as a closest bin to the right of the first bin in the histogram that has a count that is less than a second count percentage of the greatest count;
- generating an updated histogram of pixel intensity values having a range between the identified left bin and identified right bin;
- fitting a Gaussian curve onto the updated histogram;
- and
- applying a filter to the image based on parameters of the Gaussian curve.
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 M regions;
- identifying one or more of the M regions on which to perform local analysis;
- performing 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.
A computer readable medium storing code, which when executed by one or more processors cause the one or more processors to implement a method of applying a filter to an image, the code including instructions to:
- select a first region of the image;
- generate an initial histogram of pixel intensity values in the first region, wherein the histogram includes N bins;
- identify a first bin as a bin having a greatest count of pixel intensity values;
- identify a left bin as a closest bin to the left of the first bin in the histogram that has a count that is less than a first count percentage of the greatest count;
- identify a right bin as a closest bin to the right of the first bin in the histogram that has a count that is less than a second count percentage of the greatest count;
- generate an updated histogram of pixel intensity values having a range between the identified left bin and identified right bin;
- fit a Gaussian curve onto the updated histogram;
- and
- apply a filter to the image based on parameters of the Gaussian curve.
A processor-implemented method for applying a filter to an image, the method comprising:
- selecting a first region of the image;
- generating an initial histogram of pixel intensity values in the first region, wherein the histogram includes N bins;
- and
- a) identifying a first bin as a bin having a greatest count of pixel intensity values;
- b) identifying a left bin as a closest bin to the left of the first bin in the histogram that has a count that is less than a first count percentage of the greatest count;
- c) identifying a right bin as a closest bin to the right of the first bin in the histogram that has a count that is less than a second count percentage of the greatest count;
- and
- d) determining whether edges of the histogram are within a threshold percentage of the range of pixel intensity values between the identified left bin and identified right bin, and
- i) if edges of the histogram are within a threshold percentage of the range of pixel intensity values between the identified left bin and identified right bin fitting a Gaussian curve onto the histogram, applying a filter to the image based on parameters of the Gaussian curve;
- and
- ii) if edges of the histogram are not within a threshold percentage of the range of pixel intensity values between the identified left bin and identified right bin, generating an updated histogram of pixel intensity values having a range between the identified left bin and identified right bin, and repeating steps a)-d) using the updated histogram.
Dependencies are inferred from the claim text.