Discovering the human retina and its use for image processing {#tutorial_bioinspired_retina_model}
=============================================================
Goal
----
I present here a model of human retina that shows some interesting properties for image
preprocessing and enhancement. In this tutorial you will learn how to:
- discover the main two channels outing from your retina
- see the basics to use the retina model
- discover some parameters tweaks
General overview
----------------
The proposed model originates from Jeanny Herault's research @cite Herault2010 at
[Gipsa](http://www.gipsa-lab.inpg.fr). It is involved in image processing applications with
[Listic](http://www.listic.univ-savoie.fr) (code maintainer and user) lab. This is not a complete
model but it already present interesting properties that can be involved for enhanced image
processing experience. The model allows the following human retina properties to be used :
- spectral whitening that has 3 important effects: high spatio-temporal frequency signals
canceling (noise), mid-frequencies details enhancement and low frequencies luminance energy
reduction. This *all in one* property directly allows visual signals cleaning of classical
undesired distortions introduced by image sensors and input luminance range.
- local logarithmic luminance compression allows details to be enhanced even in low light
conditions.
- decorrelation of the details information (Parvocellular output channel) and transient
information (events, motion made available at the Magnocellular output channel).
The first two points are illustrated below :
In the figure below, the OpenEXR image sample *CrissyField.exr*, a High Dynamic Range image is
shown. In order to make it visible on this web-page, the original input image is linearly rescaled
to the classical image luminance range [0-255] and is converted to 8bit/channel format. Such strong
conversion hides many details because of too strong local contrasts. Furthermore, noise energy is
also strong and pollutes visual information.
In the following image, applying the ideas proposed in @cite Benoit2010, as your retina does, local
luminance adaptation, spatial noise removal and spectral whitening work together and transmit
accurate information on lower range 8bit data channels. On this picture, noise in significantly
removed, local details hidden by strong luminance contrasts are enhanced. Output image keeps its
naturalness and visual content is enhanced. Color processing is based on the color
multiplexing/demultiplexing method proposed in @cite Chaix2007 .
*Note :* image sample can be downloaded from the [OpenEXR website](http://www.openexr.com).
Regarding this demonstration, before retina processing, input image has been linearly rescaled
within 0-255 keeping its channels float format. 5% of its histogram ends has been cut (mostly
removes wrong HDR pixels). Check out the sample
*opencv/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp* for similar
processing. The following demonstration will only consider classical 8bit/channel images.
The retina model output channels
--------------------------------
The retina model presents two outputs that benefit from the above cited behaviors.
- The first one is called the Parvocellular channel. It is mainly active in the foveal retina area
(high resolution central vision with color sensitive photo-receptors), its aim is to provide
accurate color vision for visual details remaining static on the retina. On the other hand
objects moving on the retina projection are blurred.
- The second well known channel is the Magnocellular channel. It is mainly active in the retina
peripheral vision and send signals related to change events (motion, transient events, etc.).
These outing signals also help visual system to focus/center retina on 'transient'/moving areas
for more detailed analysis thus improving visual scene context and object classification.
**NOTE :** regarding the proposed model, contrary to the real retina, we apply these two channels on
the entire input images using the same resolution. This allows enhanced visual details and motion
information to be extracted on all the considered images... but remember, that these two channels
are complementary. For example, if Magnocellular channel gives strong energy in an area, then, the
Parvocellular channel is certainly blurred there since there is a transient event.
As an illustration, we apply in the following the retina model on a webcam video stream of a dark
visual scene. In this visual scene, captured in an amphitheater of the university, some students are
moving while talking to the teacher.
In this video sequence, because of the dark ambiance, signal to noise ratio is low and color
artifacts are present on visual features edges because of the low quality image capture tool-chain.
Below is shown the retina foveal vision applied on the entire image. In the used retina
configuration, global luminance is preserved and local contrasts are enhanced. Also, signal to noise
ratio is improved : since high frequency spatio-temporal noise is reduced, enhanced details are not
corrupted by any enhanced noise.
Below is the output of the Magnocellular output of the retina model. Its signals are strong where
transient events occur. Here, a student is moving at the bottom of the image thus generating high
energy. The remaining of the image is static however, it is corrupted by a strong noise. Here, the
retina filters out most of the noise thus generating low false motion area 'alarms'. This channel
can be used as a transient/moving areas detector : it would provide relevant information for a low
cost segmentation tool that would highlight areas in which an event is occurring.
Retina use case
---------------
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)
- performing motion analysis also taking benefit of the previously cited properties.
Literature
----------
For more information, refer to the following papers : @cite Benoit2010
- Please have a look at the reference work of Jeanny Herault that you can read in his book @cite Herault2010
This retina filter code includes the research contributions of phd/research colleagues 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 @cite Chaix2007
- 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.
Code tutorial
-------------
Please refer to the original tutorial source code in file
*opencv_folder/samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp*.
@note do not forget that the retina model is included in the following namespace: cv::bioinspired
To compile it, assuming OpenCV is correctly installed, use the following command. It requires the
opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video
read)* and opencv_bioinspired *(Retina description)* libraries to compile.
@code{.sh}
// compile
gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_bioinspired -lopencv_videoio -lopencv_imgcodecs
// Run commands : add 'log' as a last parameter to apply a spatial log sampling (simulates retina sampling)
// run on webcam
./Retina_tuto -video
// run on video file
./Retina_tuto -video myVideo.avi
// run on an image
./Retina_tuto -image myPicture.jpg
// run on an image with log sampling
./Retina_tuto -image myPicture.jpg log
@endcode
Here is a code explanation :
Retina definition is present in the bioinspired package and a simple include allows to use it. You
can rather use the specific header : *opencv2/bioinspired.hpp* if you prefer but then include the
other required openv modules : *opencv2/core.hpp* and *opencv2/highgui.hpp*
@code{.cpp}
#include "opencv2/opencv.hpp"
@endcode
Provide user some hints to run the program with a help function
@code{.cpp}
// the help procedure
static void help(std::string errorMessage)
{
std::cout<<"Program init error : "<<errorMessage<<std::endl;
std::cout<<"\nProgram call procedure : retinaDemo [processing mode] [Optional : media target] [Optional LAST parameter: \"log\" to activate retina log sampling]"<<std::endl;
std::cout<<"\t[processing mode] :"<<std::endl;
std::cout<<"\t -image : for still image processing"<<std::endl;
std::cout<<"\t -video : for video stream processing"<<std::endl;
std::cout<<"\t[Optional : media target] :"<<std::endl;
std::cout<<"\t if processing an image or video file, then, specify the path and filename of the target to process"<<std::endl;
std::cout<<"\t leave empty if processing video stream coming from a connected video device"<<std::endl;
std::cout<<"\t[Optional : activate retina log sampling] : an optional last parameter can be specified for retina spatial log sampling"<<std::endl;
std::cout<<"\t set \"log\" without quotes to activate this sampling, output frame size will be divided by 4"<<std::endl;
std::cout<<"\nExamples:"<<std::endl;
std::cout<<"\t-Image processing : ./retinaDemo -image lena.jpg"<<std::endl;
std::cout<<"\t-Image processing with log sampling : ./retinaDemo -image lena.jpg log"<<std::endl;
std::cout<<"\t-Video processing : ./retinaDemo -video myMovie.mp4"<<std::endl;
std::cout<<"\t-Live video processing : ./retinaDemo -video"<<std::endl;
std::cout<<"\nPlease start again with new parameters"<<std::endl;
std::cout<<"****************************************************"<<std::endl;
std::cout<<" NOTE : this program generates the default retina parameters file 'RetinaDefaultParameters.xml'"<<std::endl;
std::cout<<" => you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<<std::endl;
}
@endcode
Then, start the main program and first declare a *cv::Mat* matrix in which input images will be
loaded. Also allocate a *cv::VideoCapture* object ready to load video streams (if necessary)
@code{.cpp}
int main(int argc, char* argv[]) {
// declare the retina input buffer... that will be fed differently in regard of the input media
cv::Mat inputFrame;
cv::VideoCapture videoCapture; // in case a video media is used, its manager is declared here
@endcode
In the main program, before processing, first check input command parameters. Here it loads a first
input image coming from a single loaded image (if user chose command *-image*) or from a video
stream (if user chose command *-video*). Also, if the user added *log* command at the end of its
program call, the spatial logarithmic image sampling performed by the retina is taken into account
by the Boolean flag *useLogSampling*.
@code{.cpp}
// welcome message
std::cout<<"****************************************************"<<std::endl;
std::cout<<"* Retina demonstration : demonstrates the use of is a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
std::cout<<"* This demo will try to load the file 'RetinaSpecificParameters.xml' (if exists).\nTo create it, copy the autogenerated template 'RetinaDefaultParameters.xml'.\nThen tweak it with your own retina parameters."<<std::endl;
// basic input arguments checking
if (argc<2)
{
help("bad number of parameter");
return -1;
}
bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
std::string inputMediaType=argv[1];
//////////////////////////////////////////////////////////////////////////////
// checking input media type (still image, video file, live video acquisition)
if (!strcmp(inputMediaType.c_str(), "-image") && argc >= 3)
{
std::cout<<"RetinaDemo: processing image "<<argv[2]<<std::endl;
// image processing case
inputFrame = cv::imread(std::string(argv[2]), 1); // load image in RGB mode
}else
if (!strcmp(inputMediaType.c_str(), "-video"))
{
if (argc == 2 || (argc == 3 && useLogSampling)) // attempt to grab images from a video capture device
{
videoCapture.open(0);
}else// attempt to grab images from a video filestream
{
std::cout<<"RetinaDemo: processing video stream "<<argv[2]<<std::endl;
videoCapture.open(argv[2]);
}
// grab a first frame to check if everything is ok
videoCapture>>inputFrame;
}else
{
// bad command parameter
help("bad command parameter");
return -1;
}
@endcode
Once all input parameters are processed, a first image should have been loaded, if not, display
error and stop program :
@code{.cpp}
if (inputFrame.empty())
{
help("Input media could not be loaded, aborting");
return -1;
}
@endcode
Now, everything is ready to run the retina model. I propose here to allocate a retina instance and
to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size
object that shows the input data size that will have to be managed. One can activate other options
such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using
*enum cv::bioinspired::RETINA_COLOR_BAYER*). If using log sampling, the image reduction factor
(smaller output images) and log sampling strength can be adjusted.
@code{.cpp}
// pointer to a retina object
cv::Ptr<cv::bioinspired::Retina> myRetina;
// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
if (useLogSampling)
{
myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
myRetina = cv::bioinspired::createRetina(inputFrame.size());
@endcode
Once done, the proposed code writes a default xml file that contains the default parameters of the
retina. This is useful to make your own config using this template. Here generated template xml file
is called *RetinaDefaultParameters.xml*.
@code{.cpp}
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
myRetina->write("RetinaDefaultParameters.xml");
@endcode
In the following line, the retina attempts to load another xml file called
*RetinaSpecificParameters.xml*. If you created it and introduced your own setup, it will be loaded,
in the other case, default retina parameters are used.
@code{.cpp}
// load parameters if file exists
myRetina->setup("RetinaSpecificParameters.xml");
@endcode
It is not required here but just to show it is possible, you can reset the retina buffers to zero to
force it to forget past events.
@code{.cpp}
// reset all retina buffers (imagine you close your eyes for a long time)
myRetina->clearBuffers();
@endcode
Now, it is time to run the retina ! First create some output buffers ready to receive the two retina
channels outputs
@code{.cpp}
// declare retina output buffers
cv::Mat retinaOutput_parvo;
cv::Mat retinaOutput_magno;
@endcode
Then, run retina in a loop, load new frames from video sequence if necessary and get retina outputs
back to dedicated buffers.
@code{.cpp}
// processing loop with no stop condition
while(true)
{
// if using video stream, then, grabbing a new frame, else, input remains the same
if (videoCapture.isOpened())
videoCapture>>inputFrame;
// run retina filter on the loaded input frame
myRetina->run(inputFrame);
// Retrieve and display retina output
myRetina->getParvo(retinaOutput_parvo);
myRetina->getMagno(retinaOutput_magno);
cv::imshow("retina input", inputFrame);
cv::imshow("Retina Parvo", retinaOutput_parvo);
cv::imshow("Retina Magno", retinaOutput_magno);
cv::waitKey(10);
}
@endcode
That's done ! But if you want to secure the system, take care and manage Exceptions. The retina can
throw some when it sees irrelevant data (no input frame, wrong setup, etc.). Then, i recommend to
surround all the retina code by a try/catch system like this :
@code{.cpp}
try{
// pointer to a retina object
cv::Ptr<cv::Retina> myRetina;
[---]
// processing loop with no stop condition
while(true)
{
[---]
}
}catch(cv::Exception e)
{
std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
}
@endcode
Retina parameters, what to do ?
-------------------------------
First, it is recommended to read the reference paper @cite Benoit2010
Once done open the configuration file *RetinaDefaultParameters.xml* generated by the demo and let's
have a look at it.
@code{.cpp}
<?xml version="1.0"?>
<opencv_storage>
<OPLandIPLparvo>
<colorMode>1</colorMode>
<normaliseOutput>1</normaliseOutput>
<photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
<photoreceptorsSpatialConstant>5.7e-01</photoreceptorsSpatialConstant>
<horizontalCellsGain>0.01</horizontalCellsGain>
<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
<hcellsSpatialConstant>7.</hcellsSpatialConstant>
<ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
<IPLmagno>
<normaliseOutput>1</normaliseOutput>
<parasolCells_beta>0.</parasolCells_beta>
<parasolCells_tau>0.</parasolCells_tau>
<parasolCells_k>7.</parasolCells_k>
<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
<V0CompressionParameter>9.5e-01</V0CompressionParameter>
<localAdaptintegration_tau>0.</localAdaptintegration_tau>
<localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
</opencv_storage>
@endcode
Here are some hints but actually, the best parameter setup depends more on what you want to do with
the retina rather than the images input that you give to retina. Apart from the more specific case
of High Dynamic Range images (HDR) that require more specific setup for specific luminance
compression objective, the retina behaviors should be rather stable from content to content. Note
that OpenCV is able to manage such HDR format thanks to the OpenEXR images compatibility.
Then, if the application target requires details enhancement prior to specific image processing, you
need to know if mean luminance information is required or not. If not, the the retina can cancel or
significantly reduce its energy thus giving more visibility to higher spatial frequency details.
#### Basic parameters
The simplest parameters are as follows :
- **colorMode** : let the retina process color information (if 1) or gray scale images (if 0). In
that last case, only the first channels of the input will be processed.
- **normaliseOutput** : each channel has such parameter: if the value is set to 1, then the considered
channel's output is rescaled between 0 and 255. Be aware at this case of the Magnocellular output
level (motion/transient channel detection). Residual noise will also be rescaled !
**Note :** using color requires color channels multiplexing/demultipexing which also demands more
processing. You can expect much faster processing using gray levels : it would require around 30
product per pixel for all of the retina processes and it has recently been parallelized for multicore
architectures.
#### Photo-receptors parameters
The following parameters act on the entry point of the retina - photo-receptors - and has impact on all
of the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and
spatial data and also adjust their sensitivity to local luminance,thus, leads to improving details extraction
and high frequency noise canceling.
- **photoreceptorsLocalAdaptationSensitivity** between 0 and 1. Values close to 1 allow high
luminance log compression's effect at the photo-receptors level. Values closer to 0 provide a more
linear sensitivity. Increased alone, it can burn the *Parvo (details channel)* output image. If
adjusted in collaboration with **ganglionCellsSensitivity**,images can be very contrasted
whatever the local luminance there is... at the cost of a naturalness decrease.
- **photoreceptorsTemporalConstant** this setups the temporal constant of the low pass filter
effect at the entry of the retina. High value leads to strong temporal smoothing effect : moving
objects are blurred and can disappear while static object are favored. But when starting the
retina processing, stable state is reached later.
- **photoreceptorsSpatialConstant** specifies the spatial constant related to photo-receptors' low
pass filter's effect. Those parameters specify the minimum value of the spatial signal period allowed
in what follows. Typically, this filter should cut high frequency noise. On the other hand, a 0 value
cuts none of the noise while higher values start to cut high spatial frequencies, and progressively
lower frequencies... Be aware to not go to high levels if you want to see some details of the input images !
A good compromise for color images is a 0.53 value since such choice won't affect too much the color spectrum.
Higher values would lead to gray and blurred output images.
#### Horizontal cells parameters
This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells.
It modulates photo-receptors sensitivity and completes the processing for final spectral whitening
(part of the spatial band pass effect thus favoring visual details enhancement).
- **horizontalCellsGain** here is a critical parameter ! If you are not interested with the mean
luminance and want just to focus on details enhancement, then, set this parameterto zero. However, if
you want to keep some environment luminance's data, let some low spatial frequencies pass into the system and set a
higher value (\<1).
- **hcellsTemporalConstant** similar to photo-receptors, this parameter acts on the temporal constant of a
low pass temporal filter that smoothes input data. Here, a high value generates a high retina
after effect while a lower value makes the retina more reactive. This value should be lower than
**photoreceptorsTemporalConstant** to limit strong retina after effects.
- **hcellsSpatialConstant** is the spatial constant of these cells filter's low pass one.
It specifies the lowest spatial frequency allowed in what follows. Visually, a high value leads
to very low spatial frequencies processing and leads to salient halo effects. Lower values
reduce this effect but has the limit of not go lower than the value of
**photoreceptorsSpatialConstant**. Those 2 parameters actually specify the spatial band-pass of
the retina.
**NOTE** Once the processing managed by the previous parameters is done, input data is cleaned from noise
and luminance is already partly enhanced. The following parameters act on the last processing stages
of the two outing retina signals.
#### Parvo (details channel) dedicated parameter
- **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at
the output of this details' dedicated channel. Parameter values remain between 0 and 1. Low value
tend to give a linear response while higher values enforce the remaining low contrasted areas.
**Note :** this parameter can correct eventual burned images by favoring low energetic details of
the visual scene, even in bright areas.
#### IPL Magno (motion/transient channel) parameters
Once image's information are cleaned, this channel acts as a high pass temporal filter that
selects only the signals related to transient signals (events, motion, etc.). A low pass spatial filter
smoothes extracted transient data while a final logarithmic compression enhances low transient events
thus enhancing event sensitivity.
- **parasolCells_beta** generally set to zero, can be considered as an amplifier gain at the
entry point of this processing stage. Generally set to 0.
- **parasolCells_tau** the temporal smoothing effect that can be added
- **parasolCells_k** the spatial constant of the spatial filtering effect, set it at a high value
to favor low spatial frequency signals that are lower subject for residual noise.
- **amacrinCellsTemporalCutFrequency** specifies the temporal constant of the high pass filter.
High values let slow transient events to be selected.
- **V0CompressionParameter** specifies the strength of the log compression. Similar behaviors to
previous description but here enforces sensitivity of transient events.
- **localAdaptintegration_tau** generally set to 0, has no real use actually in here.
- **localAdaptintegration_k** specifies the size of the area on which local adaptation is
performed. Low values lead to short range local adaptation (higher sensitivity to noise), high
values secure log compression.