HDR imaging

Classes
class	cv::AlignExposures
	The base class for algorithms that align images of the same scene with different exposures. More...
class	cv::AlignMTB
	This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. More...
class	cv::CalibrateCRF
	The base class for camera response calibration algorithms. More...
class	cv::CalibrateDebevec
	Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. Objective function is constructed using pixel values on the same position in all images, extra term is added to make the result smoother. More...
class	cv::CalibrateRobertson
	Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. This algorithm uses all image pixels. More...
class	cv::MergeDebevec
	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response. More...
class	cv::MergeExposures
	The base class algorithms that can merge exposure sequence to a single image. More...
class	cv::MergeMertens
	Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids. More...
class	cv::MergeRobertson
	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response. More...
class	cv::Tonemap
	Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. More...
class	cv::TonemapDrago
	Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain. More...
class	cv::TonemapMantiuk
	This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, transforms contrast values to HVS response and scales the response. After this the image is reconstructed from new contrast values. More...
class	cv::TonemapReinhard
	This is a global tonemapping operator that models human visual system. More...

Enumerations
enum	{ cv::LDR_SIZE = 256 }

Functions
Ptr< AlignMTB >	cv::createAlignMTB (int max_bits=6, int exclude_range=4, bool cut=true)
	Creates AlignMTB object. More...
Ptr< CalibrateDebevec >	cv::createCalibrateDebevec (int samples=70, float lambda=10.0f, bool random=false)
	Creates CalibrateDebevec object. More...
Ptr< CalibrateRobertson >	cv::createCalibrateRobertson (int max_iter=30, float threshold=0.01f)
	Creates CalibrateRobertson object. More...
Ptr< MergeDebevec >	cv::createMergeDebevec ()
	Creates MergeDebevec object. More...
Ptr< MergeMertens >	cv::createMergeMertens (float contrast_weight=1.0f, float saturation_weight=1.0f, float exposure_weight=0.0f)
	Creates MergeMertens object. More...
Ptr< MergeRobertson >	cv::createMergeRobertson ()
	Creates MergeRobertson object. More...
Ptr< Tonemap >	cv::createTonemap (float gamma=1.0f)
	Creates simple linear mapper with gamma correction. More...
Ptr< TonemapDrago >	cv::createTonemapDrago (float gamma=1.0f, float saturation=1.0f, float bias=0.85f)
	Creates TonemapDrago object. More...
Ptr< TonemapMantiuk >	cv::createTonemapMantiuk (float gamma=1.0f, float scale=0.7f, float saturation=1.0f)
	Creates TonemapMantiuk object. More...
Ptr< TonemapReinhard >	cv::createTonemapReinhard (float gamma=1.0f, float intensity=0.0f, float light_adapt=1.0f, float color_adapt=0.0f)
	Creates TonemapReinhard object. More...

Detailed Description

This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, camera calibration with multiple exposures and exposure fusion.

Enumeration Type Documentation

anonymous enum

anonymous enum

Enumerator
LDR_SIZE  Python: cv.LDR_SIZE

Function Documentation

createAlignMTB()

Ptr<AlignMTB> cv::createAlignMTB	(	int	max_bits = 6,
		int	exclude_range = 4,
		bool	cut = true
	)

Python:
	retval	=	cv.createAlignMTB(	[, max_bits[, exclude_range[, cut]]]	)

Creates AlignMTB object.

Parameters

max_bits	logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively).
exclude_range	range for exclusion bitmap that is constructed to suppress noise around the median value.
cut	if true cuts images, otherwise fills the new regions with zeros.

createCalibrateDebevec()

Ptr<CalibrateDebevec> cv::createCalibrateDebevec	(	int	samples = 70,
		float	lambda = 10.0f,
		bool	random = false
	)

Python:
	retval	=	cv.createCalibrateDebevec(	[, samples[, lambda[, random]]]	)

Creates CalibrateDebevec object.

Parameters

samples	number of pixel locations to use
lambda	smoothness term weight. Greater values produce smoother results, but can alter the response.
random	if true sample pixel locations are chosen at random, otherwise they form a rectangular grid.

createCalibrateRobertson()

Ptr<CalibrateRobertson> cv::createCalibrateRobertson	(	int	max_iter = 30,
		float	threshold = 0.01f
	)

Python:
	retval	=	cv.createCalibrateRobertson(	[, max_iter[, threshold]]	)

Creates CalibrateRobertson object.

Parameters

max_iter	maximal number of Gauss-Seidel solver iterations.
threshold	target difference between results of two successive steps of the minimization.

createMergeDebevec()

Ptr<MergeDebevec> cv::createMergeDebevec

(

)

Python:
	retval	=	cv.createMergeDebevec(		)

Creates MergeDebevec object.

createMergeMertens()

Ptr<MergeMertens> cv::createMergeMertens	(	float	contrast_weight = 1.0f,
		float	saturation_weight = 1.0f,
		float	exposure_weight = 0.0f
	)

Python:
	retval	=	cv.createMergeMertens(	[, contrast_weight[, saturation_weight[, exposure_weight]]]	)

Creates MergeMertens object.

Parameters

contrast_weight	contrast measure weight. See MergeMertens.
saturation_weight	saturation measure weight
exposure_weight	well-exposedness measure weight

createMergeRobertson()

Ptr<MergeRobertson> cv::createMergeRobertson

(

)

Python:
	retval	=	cv.createMergeRobertson(		)

Creates MergeRobertson object.

createTonemap()

Ptr<Tonemap> cv::createTonemap

(

float

gamma = 1.0f

)

Python:
	retval	=	cv.createTonemap(	[, gamma]	)

Creates simple linear mapper with gamma correction.

Parameters

gamma

positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it.

createTonemapDrago()

Ptr<TonemapDrago> cv::createTonemapDrago	(	float	gamma = 1.0f,
		float	saturation = 1.0f,
		float	bias = 0.85f
	)

Python:
	retval	=	cv.createTonemapDrago(	[, gamma[, saturation[, bias]]]	)

Creates TonemapDrago object.

Parameters

gamma	gamma value for gamma correction. See createTonemap
saturation	positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it.
bias	value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85.

createTonemapMantiuk()

Ptr<TonemapMantiuk> cv::createTonemapMantiuk	(	float	gamma = 1.0f,
		float	scale = 0.7f,
		float	saturation = 1.0f
	)

Python:
	retval	=	cv.createTonemapMantiuk(	[, gamma[, scale[, saturation]]]	)

Creates TonemapMantiuk object.

Parameters

gamma	gamma value for gamma correction. See createTonemap
scale	contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results.
saturation	saturation enhancement value. See createTonemapDrago

createTonemapReinhard()

Ptr<TonemapReinhard> cv::createTonemapReinhard	(	float	gamma = 1.0f,
		float	intensity = 0.0f,
		float	light_adapt = 1.0f,
		float	color_adapt = 0.0f
	)

Python:
	retval	=	cv.createTonemapReinhard(	[, gamma[, intensity[, light_adapt[, color_adapt]]]]	)

Creates TonemapReinhard object.

Parameters

gamma	gamma value for gamma correction. See createTonemap
intensity	result intensity in [-8, 8] range. Greater intensity produces brighter results.
light_adapt	light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases.
color_adapt	chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel.