a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.
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| virtual void | applyFastToneMapping (InputArray inputImage, OutputArray outputToneMappedImage)=0 |
| | applies a luminance correction (initially High Dynamic Range (HDR) tone mapping) More...
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| 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...
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| | Algorithm () |
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| virtual | ~Algorithm () |
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| virtual void | clear () |
| | Clears the algorithm state. More...
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| virtual bool | empty () const |
| | Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
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| virtual String | getDefaultName () const |
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| virtual void | read (const FileNode &fn) |
| | Reads algorithm parameters from a file storage. More...
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| virtual void | save (const String &filename) const |
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| virtual void | write (FileStorage &fs) const |
| | Stores algorithm parameters in a file storage. More...
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| void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| | simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
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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.
| virtual void cv::bioinspired::RetinaFastToneMapping::applyFastToneMapping |
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InputArray |
inputImage, |
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OutputArray |
outputToneMappedImage |
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pure virtual |
| Python: |
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| 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
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| inputImage | the input image to process RGB or gray levels |
| outputToneMappedImage | the output tone mapped image |
Public Member Functions inherited from cv::Algorithm
1.8.13