Using luminance distributions to detect best areas of an image for prediction of noise levels

Patent metadata

Field	Value
Publication number	US-2023410273-A1
Application number	US-202118035712-A
Country	US
Kind code	A1
Filing date	Nov 8, 2021
Priority date	Nov 9, 2020
Publication date	Dec 21, 2023
Grant date	—

Abstract

An image processing apparatus and method are provided whereby one or more memories stores instructions which, when executed by one or more processors configures the one or more processors to perform operations including obtaining image data stored in memory of a processing device, defining one or more regions of the image to be processed based on luminance values of the region, providing, as input data, the one or more defined regions of the image to an classifier that has been trained to use image data to estimate noise in an image to output a prediction that the input data is in a first class or a second class, calculating an average by predicted class, and labeling the obtained image as the first class or second class based on the calculated average.

First claim

1 . An image processing method comprising:
obtaining image data stored in memory of a processing device; defining one or more regions of the image to be processed based on luminance values of the region; providing, as input data, the one or more defined regions of the image to an classifier that has been trained to use image data to estimate noise in an image to output a prediction that the input data is in a first class or a second class; calculating an average by predicted class; and labeling the obtained image as the first class or second class based on the calculated average.

2 . The image processing method of claim 1 , further comprising
dividing the obtained image data into predetermined segments of image data; and wherein the defining of one or more regions of the image is performed within each of the predetermined segments.

3 . The image processing method of claim 2 , further comprising
providing, as the input data, one or more defined regions in each of the predetermined segments to the classifier to output predictions for each of the one or more defined regions in each of the segments; and calculating the average by predicted class across all of the predetermined segments to generate the label for the image.

4 . The image processing method of claim 1 , further comprising
defining one or more regions of the image to be processed based on luminance values of the region by subtracting each value of a determined luminance array of the image from a central luminance value to identify a central point around which a respective one of the one or more regions is defined.

5 . The image processing method of claim 4 , further comprising
generating a bounding box having a predetermined size having the identified central point at a center; and providing the image data within the generated bounding box to the classifier.

6 . The image processing method of claim 4 , wherein
the central luminance value is a luminance value closest to a median luminance value of the image.

7 . The image processing method of claim 1 , further comprising
outputting, on a display, the obtained image including the label.

8 . An image processing apparatus comprising:
one or more memories having instructions stored therein; and one or more processors that, upon executing the stored instructions, configures the one or more processors to perform the following operations:
obtaining image data stored in memory of a processing device;
defining one or more regions of the image to be processed based on luminance values of the region;
providing, as input data, the one or more defined regions of the image to an classifier that has been trained to use image data to estimate noise in an image to output a prediction that the input data is in a first class or a second class;
calculating an average by predicted class; and
labeling the obtained image as the first class or second class based on the calculated average.

9 . The image processing apparatus of claim 8 , wherein execution of the stored instructions further configures the one or more processors to perform operations including
dividing the obtained image data into predetermined segments of image data; and wherein the defining of one or more regions of the image is performed within each of the predetermined segments.

10 . The image processing apparatus of claim 9 , wherein execution of the stored instructions further configures the one or more processors to perform operations including
providing, as the input data, one or more defined regions in each of the predetermined segments to the classifier to output predictions for each of the one or more defined regions in each of the segments; and calculating the average by predicted class across all of the predetermined segments to generate the label for the image.

11 . The image processing apparatus of claim 8 , wherein execution of the stored instructions further configures the one or more processors to perform operations including
defining one or more regions of the image to be processed based on luminance values of the region by subtracting each value of a determined luminance array of the image from a central luminance value to identify a central point around which a respective one of the one or more regions is defined.

12 . The image processing apparatus of claim 11 , wherein execution of the stored instructions further configures the one or more processors to perform operations including
generating a bounding box having a predetermined size having the identified central point at a center; and providing the image data within the generated bounding box to the classifier.

13 . The image processing apparatus of claim 11 , wherein
the central luminance value is a luminance value closest to a median luminance value of the image.

14 . The image processing apparatus of claim 8 , wherein execution of the stored instructions further configures the one or more processors to perform operations including
outputting, on a display, the obtained image including the label.

Assignees

• Canon Usa Inc

Inventors

• Guyduy Yevgeniy Gennadiy

Classifications

• G06T7/0002 Primary

  Inspection of images, e.g. flaw detection · CPC title

• G06T7/11

  Region-based segmentation · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/30168

  Image quality inspection · CPC title

• G06V10/25

  Determination of region of interest [ROI] or a volume of interest [VOI] · CPC title