Using luminance distributions to detect best areas of an image for prediction of noise levels
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.
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 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;
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
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.
5 . The image processing method of claim 1 , wherein
the central luminance value is a luminance value closest to a median luminance value of the image.
6 . The image processing method of claim 1 , further comprising outputting, on a display, the obtained image including the label.
7 . 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 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;
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.
8 . The image processing apparatus of claim 7 , 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.
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
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.
10 . The image processing apparatus of claim 7 , 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.
11 . The image processing apparatus of claim 8 , wherein
the central luminance value is a luminance value closest to a median luminance value of the image.
12 . 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.