diff --git a/modules/bioinspired/doc/retina.markdown b/modules/bioinspired/doc/retina.markdown
index 14e68516538..a508c43e64c 100644
--- a/modules/bioinspired/doc/retina.markdown
+++ b/modules/bioinspired/doc/retina.markdown
@@ -120,7 +120,7 @@ We present here some illustrations of the retina outputs on some examples taken
 Also take into account that the two retina outputs are rescaled in range [0:255] such that magno output can show a lot of "noise" when nothing moves while drawing it. However, its energy remains low if you retrieve it using *getMagnoRAW* getter instead.
 
 
-![Retina processing on RGB image sequence : example from http://changedetection.net/ (baseline/PETS2006). Parvo enforces static signals but smooths moving persons since they do not remain static from its point of view. Magno channel highligths moving persons, observe the energy mapping on the one on top, partly behind a dark glass.](images/VideoDemo_RGB_PETS2006.jpg)
+![Retina processing on RGB image sequence : example from http://changedetection.net/ (baseline/PETS2006). Parvo enforces static signals but smooths moving persons since they do not remain static from its point of view. Magno channel highlights moving persons, observe the energy mapping on the one on top, partly behind a dark glass.](images/VideoDemo_RGB_PETS2006.jpg)
 
 ![Retina processing on gray levels image sequence : example from http://changedetection.net/ (thermal/park). On such grayscale images, parvo channel enforces contrasts while magno strongly reacts on moving pedestrians](images/VideoDemo_thermal_park.jpg)
 
diff --git a/modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp b/modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp
index ba1a87292e8..8820352e025 100644
--- a/modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp
+++ b/modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp
@@ -24,7 +24,7 @@
  **
  **
  **
- ** 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:
+ ** 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 light 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
  **
  **
diff --git a/modules/bioinspired/src/retinafasttonemapping.cpp b/modules/bioinspired/src/retinafasttonemapping.cpp
index 0bf9375bb35..74f8906fefb 100644
--- a/modules/bioinspired/src/retinafasttonemapping.cpp
+++ b/modules/bioinspired/src/retinafasttonemapping.cpp
@@ -21,7 +21,7 @@
  ** 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:
+ ** 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 light 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
  **
  **