Class cv::bioinspired::RetinaFastToneMapping#

a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV. View details

#include <opencv2/bioinspired/retinafasttonemapping.hpp>

Collaboration diagram for cv::bioinspired::RetinaFastToneMapping:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.

This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc. As a summary, these are the model properties:

2 stages of local luminance adaptation with a different local neighborhood for each.
first stage models the retina photorecetors local luminance adaptation
second stage models th ganglion cells local information adaptation
compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters. this can help noise robustness and temporal stability for video sequence use cases.

for more information, read to the following papers : Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit A., Caplier A., Durette B., Herault, J., “USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING”, Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 regarding spatio-temporal filter and the bigger retina model : Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.

Member Function Documentation#

applyFastToneMapping()#

void cv::bioinspired::RetinaFastToneMapping::applyFastToneMapping(
InputArray inputImage,
OutputArray outputToneMappedImage )

Python:

cv.bioinspired.RetinaFastToneMapping.applyFastToneMapping(inputImage[, outputToneMappedImage]) -> outputToneMappedImage

applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)

using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal smoothing and eventually high frequencies attenuation. This is a lighter method than the one available using the regular retina::run method. It is then faster but it does not include complete temporal filtering nor retina spectral whitening. Then, it can have a more limited effect on images with a very high dynamic range. This is an adptation of the original still image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan’s work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816

Parameters

inputImage — the input image to process RGB or gray levels
outputToneMappedImage — the output tone mapped image

setup()#

void cv::bioinspired::RetinaFastToneMapping::setup(
const float photoreceptorsNeighborhoodRadius = 3.f,
const float ganglioncellsNeighborhoodRadius = 1.f,
const float meanLuminanceModulatorK = 1.f )

Python:

cv.bioinspired.RetinaFastToneMapping.setup([, photoreceptorsNeighborhoodRadius[, ganglioncellsNeighborhoodRadius[, meanLuminanceModulatorK]]])

updates tone mapping behaviors by adjusing the local luminance computation area

Parameters

photoreceptorsNeighborhoodRadius — the first stage local adaptation area
ganglioncellsNeighborhoodRadius — the second stage local adaptation area
meanLuminanceModulatorK — the factor applied to modulate the meanLuminance information (default is 1, see reference paper)

create()#

static Ptr< RetinaFastToneMapping > cv::bioinspired::RetinaFastToneMapping::create(Size inputSize)

Python:

cv.bioinspired.RetinaFastToneMapping.create(inputSize) -> retval

cv.bioinspired.RetinaFastToneMapping_create(inputSize) -> retval

Source file#

The documentation for this class was generated from the following file:

opencv2/bioinspired/retinafasttonemapping.hpp