Adaptive noise filter
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.
1. 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;
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.
2. A method according to claim 1 , wherein the dividing the input image into the plurality of regions comprises superimposing a grid onto the input image.
3. A method according to claim 2 , wherein the one or more identified regions comprises a plurality of regions proximal to a center of the grid.
4. A method according to claim 2 , wherein the one or more identified regions comprises a plurality of regions proximal to outside edges of the grid.
5. A method according to claim 1 , wherein the performing the local analysis on a region comprises fitting a Gaussian curve to a histogram of pixel intensity associated with the region.
6. A method according to claim 1 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.
7. 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;
wherein the instructions to select one of the one or more identified regions as the representative region include instructions to:
for each of the one or more identified regions:
fit a Gaussian curve to a histogram of pixel intensity values of the region,
determine whether a first percentage of all of the pixel intensity values in the region is accounted for in the histogram,
determine whether a second percentage of all of the pixel intensity values in the region is below or within the Gaussian curve in the histogram,
determine whether a third percentage of all of the pixel intensity values in the region is outside the Gaussian curve in the histogram;
assign a quality score to each of the one or more identified regions; and
select one region as the representative region based on the quality scores.
8. The non-transitory computer readable medium of claim 7 , wherein the instructions to divide the input image into the plurality of regions comprises instructions to superimpose a grid onto the input image.
9. The non-transitory computer readable medium of claim 8 , wherein the one or more identified regions comprises a plurality of regions proximal to a center of the grid.
10. The non-transitory computer readable medium of claim 8 wherein the one or more identified regions comprises a plurality of regions proximal to outside edges of the grid.
11. The non-transitory computer readable medium of claim 7 , wherein the instructions to perform the local analysis on a region comprises instructions to fit a Gaussian curve to a histogram of pixel intensity associated with the region.
12. The non-transitory computer readable medium of claim 7 , wherein the instructions to select one of the one or more identified regions as the representative region comprises instructions to calculate an intensity match ratio or a frequency match ratio.