Method and systems for adaptive pixel correction
Methods and systems are provided for correcting pixel intensity variation in an image. In one example, a method comprises: acquiring reference images at different sensor temperatures and/or exposure times; generating a plurality of parameters via identifying a cut-off pixel number using a break-up point between a linear region and a non-linear region of an intensity curve for each reference image; sending coordinates of identified pixels to be corrected to an image sensor; capturing a pixel-corrected image via the image sensor by replacing a pixel intensity at coordinates of the pixels to be corrected with a local statistic intensity; and outputting the pixel-corrected image for display and/or storage.
1 . A method for pixel correction of an imaging system, comprising:
obtaining a reference image with the imaging system configured according to imaging conditions;
removing pixels of the reference image having a brightness equal to a maximum intensity from a dataset of the reference image;
plotting an intensity distribution curve of the dataset of the reference image;
modeling the intensity distribution curve using a curve fitting algorithm;
identifying a break-up point between a linear region and a non-linear region of the intensity distribution curve using a decision tree algorithm;
obtaining a set of model parameters for the intensity distribution curve in the linear region based on results of the decision tree algorithm;
generating a parameter model using the set of model parameters; and
storing the parameter model in at least one memory of a computing device in the imaging system.
2 . The method of claim 1 , further comprising obtaining multiple reference images with the imaging system configured according to different imaging conditions.
3 . The method of claim 2 , wherein the different imaging conditions include different sensor temperature and fixed exposure time of the imaging system, fixed sensor temperature and different exposure time of the imaging system, and/or different sensor temperature and different exposure time of the imaging system.
4 . The method of claim 1 , wherein plotting the intensity distribution curve comprises numbering each pixel, storing coordinates of a pixel with a corresponding pixel number, and sorting numbered pixels of the reference image by intensity.
5 . The method of claim 1 , wherein modeling the intensity distribution curve using the curve fitting algorithm comprises identifying a transition between the linear region and the non-linear region of the intensity distribution curve using decision tree algorithm.
6 . The method of claim 1 , wherein obtaining the set of model parameters comprises obtaining a slope and a y-intercept of the linear region.
7 . The method of claim 6 , wherein generating the parameter model using the model parameters comprises modeling the slope and the y-intercept of linear regions of intensity curves captured for different imaging conditions derived by a single, three dimensional (3D) model.
8 . The method of claim 6 , further comprising:
identifying sensor operating conditions of an image sensor of the imaging system;
identifying coordinates of pixels to be corrected using the sensor operating conditions and the set of model parameters; and
automatically correcting pixels captured in a raw image by the imaging system via the image sensor by replacing a pixel intensity at coordinates of the pixels to be corrected with a local statistic intensity.
9 . The method of claim 8 , further comprising outputting a pixel-corrected image for display and/or storage.