Retina : a Bio mimetic human retina model
Retina
Class which provides the main controls to the Gipsa/Listic labs human retina model. Spatio-temporal filtering modelling the two main retina information channels :
- foveal vision for detailled color vision : the parvocellular pathway).
- periphearal vision for sensitive transient signals detection (motion and events) : the magnocellular pathway.
NOTE : See the Retina tutorial in the tutorial/contrib section for complementary explanations.
The retina can be settled up with various parameters, by default, the retina cancels mean luminance and enforces all details of the visual scene. In order to use your own parameters, you can use at least one time the write(std::string fs) method which will write a proper XML file with all default parameters. Then, tweak it on your own and reload them at any time using method setup(std::string fs). These methods update a Retina::RetinaParameters member structure that is described hereafter.
class Retina
{
public:
// parameters setup instance
struct RetinaParameters; // this class is detailled later
// constructors
Retina (Size inputSize);
Retina (Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
// main method for input frame processing
void run (const Mat &inputImage);
// output buffers retreival methods
// -> foveal color vision details channel with luminance and noise correction
void getParvo (Mat &retinaOutput_parvo);
void getParvo (std::valarray< float > &retinaOutput_parvo);
const std::valarray< float > & getParvo () const;
// -> peripheral monochrome motion and events (transient information) channel
void getMagno (Mat &retinaOutput_magno);
void getMagno (std::valarray< float > &retinaOutput_magno);
const std::valarray< float > & getMagno () const;
// reset retina buffers... equivalent to closing your eyes for some seconds
void clearBuffers ();
// retreive input and output buffers sizes
Size inputSize ();
Size outputSize ();
// setup methods with specific parameters specification of global xml config file loading/write
void setup (std::string retinaParameterFile="", const bool applyDefaultSetupOnFailure=true);
void setup (FileStorage &fs, const bool applyDefaultSetupOnFailure=true);
void setup (RetinaParameters newParameters);
struct Retina::RetinaParameters getParameters ();
const std::string printSetup ();
virtual void write (std::string fs) const;
virtual void write (FileStorage &fs) const;
void setupOPLandIPLParvoChannel (const bool colorMode=true, const bool normaliseOutput=true, const float photoreceptorsLocalAdaptationSensitivity=0.7, const float photoreceptorsTemporalConstant=0.5, const float photoreceptorsSpatialConstant=0.53, const float horizontalCellsGain=0, const float HcellsTemporalConstant=1, const float HcellsSpatialConstant=7, const float ganglionCellsSensitivity=0.7);
void setupIPLMagnoChannel (const bool normaliseOutput=true, const float parasolCells_beta=0, const float parasolCells_tau=0, const float parasolCells_k=7, const float amacrinCellsTemporalCutFrequency=1.2, const float V0CompressionParameter=0.95, const float localAdaptintegration_tau=0, const float localAdaptintegration_k=7);
void setColorSaturation (const bool saturateColors=true, const float colorSaturationValue=4.0);
void activateMovingContoursProcessing (const bool activate);
void activateContoursProcessing (const bool activate);
};
Description
Class which allows the Gipsa (preliminary work) / Listic (code maintainer) labs retina model to be used. This class allows human retina spatio-temporal image processing to be applied on still images, images sequences and video sequences. Briefly, here are the main human retina model properties:
- spectral whithening (mid-frequency details enhancement)
- high frequency spatio-temporal noise reduction (temporal noise and high frequency spatial noise are minimized)
- low frequency luminance reduction (luminance range compression) : high luminance regions do not hide details in darker regions anymore
- local logarithmic luminance compression allows details to be enhanced even in low light conditions
Use : this model can be used basically for spatio-temporal video effects but also in the aim of :
- performing texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges (check out the parvocellular retina channel output, by using the provided getParvo methods)
- performing motion analysis also taking benefit of the previously cited properties (check out the magnocellular retina channel output, by using the provided getMagno methods)
For more information, refer to the following papers :
- 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>
- Please have a look at the reference work of Jeanny Herault that you can read in 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 retina filter code includes the research contributions of phd/research collegues from which code has been redrawn by the author :
- take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene phD color mosaicing/demosaicing and his reference paper: B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
- take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. ====> more informations in the above cited Jeanny Heraults's book.
Demos and experiments !
NOTE : Complementary to the following examples, have a look at the Retina tutorial in the tutorial/contrib section for complementary explanations.
Take a look at the provided C++ examples provided with OpenCV :
-
- samples/cpp/retinademo.cpp shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video.
-
Typical uses are (provided your OpenCV installation is situated in folder OpenCVReleaseFolder)
- image processing : OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg
- video processing : OpenCVReleaseFolder/bin/retinademo -video myMovie.avi
- webcam processing: OpenCVReleaseFolder/bin/retinademo -video
Note : This demo generates the file RetinaDefaultParameters.xml which contains the default parameters of the retina. Then, rename this as RetinaSpecificParameters.xml, adjust the parameters the way you want and reload the program to check the effect.
-
samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp shows how to use the retina to perform High Dynamic Range (HDR) luminance compression
Then, take a HDR image using bracketing with your camera and generate an OpenEXR image and then process it using the demo.
Typical use, supposing that you have the OpenEXR image memorial.exr (present in the samples/cpp/ folder)
OpenCVReleaseFolder/bin/OpenEXRimages_HighDynamicRange_Retina_toneMapping memorial.exr
Note that some sliders are made available to allow you to play with luminance compression.
Methods description
Here are detailled the main methods to control the retina model