libopencv-contrib-dev 3.2.0+dfsg-4build2 / usr / include / opencv2 / bioinspired / retinafasttonemapping.hpp

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 **
 ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
 **
 ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
 **
 **  Creation - enhancement process 2007-2013
 **      Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
 **
 ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
 ** Refer to the following research paper for more information:
 ** Benoit 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
 ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
 ** 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.
 **
 **
 **
 **
 **
 ** This class is based on image processing tools of the author and already used within the Retina class (this is the same code as method retina::applyFastToneMapping, but in an independent class, it is ligth from a memory requirement point of view). It implements an adaptation of the efficient tone mapping algorithm propose by 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
 **
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 ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
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#ifndef __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__
#define __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__

/**
@file
@date May 26, 2013
@author Alexandre Benoit
 */

#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support

namespace cv{
namespace bioinspired{

//! @addtogroup bioinspired
//! @{

/** @brief  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.
*/
class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm
{
public:

    /** @brief 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

    @param inputImage the input image to process RGB or gray levels
    @param outputToneMappedImage the output tone mapped image
    */
    CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0;

    /** @brief updates tone mapping behaviors by adjusing the local luminance computation area

    @param photoreceptorsNeighborhoodRadius the first stage local adaptation area
    @param ganglioncellsNeighborhoodRadius the second stage local adaptation area
    @param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information
    (default is 1, see reference paper)
     */
    CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0;
};

//! @relates bioinspired::RetinaFastToneMapping
CV_EXPORTS_W Ptr<RetinaFastToneMapping> createRetinaFastToneMapping(Size inputSize);

//! @}

}
}
#endif /* __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ */