cv.LearningBasedWB

More sophisticated learning-based automatic white balance algorithm

As cv.GrayworldWB, this algorithm works by applying different gains to the input image channels, but their computation is a bit more involved compared to the simple gray-world assumption. More details about the algorithm can be found in [Cheng2015].

To mask out saturated pixels this function uses only pixels that satisfy the following condition:

max(R,G,B)/RangeMaxVal < SaturationThreshold

Currently supports RGB images of type uint8 and uint16.

References

[Cheng2015]:

Dongliang Cheng, Brian Price, Scott Cohen, and Michael S Brown. "Effective learning-based illuminant estimation using simple features". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1000-1008, 2015.

Superclasses	handle
Sealed	false
Construct on load	false

HistBinNum	Defines the size of one dimension of a three-dimensional RGB
RangeMaxVal	Maximum possible value of the input image (e.g. 255 for 8 bit images,
SaturationThreshold	Threshold that is used to determine saturated pixels, i.e. pixels
id	Object ID

	addlistener	Add listener for event.
	balanceWhite	Applies white balancing to the input image
	delete	Destructor
	eq	== (EQ) Test handle equality.
	extractSimpleFeatures	Implements the feature extraction part of the algorithm
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	gt	> (GT) Greater than relation for handles.
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	lt	< (LT) Less than relation for handles.
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.