cv::bioinspired::RetinaFastToneMapping Class Reference

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

#include "retinafasttonemapping.hpp"

Inheritance diagram for cv::bioinspired::RetinaFastToneMapping:

Public Member Functions
virtual void	applyFastToneMapping (InputArray inputImage, OutputArray outputToneMappedImage)=0
	applies a luminance correction (initially High Dynamic Range (HDR) tone mapping) More...
virtual void	setup (const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0
	updates tone mapping behaviors by adjusing the local luminance computation area More...
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state. More...
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
static Ptr< RetinaFastToneMapping >	create (Size inputSize)
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

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()

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

pure virtual

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

create()

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

static

setup()

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

pure virtual

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)

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

• /build/master-contrib_docs-lin64/opencv_contrib/modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp