Image shadow removal using pulse coupled neural network

This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength /spl beta/). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.

[1]  Reinhard Eckhorn,et al.  Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[2]  John L. Johnson,et al.  PCNN models and applications , 1999, IEEE Trans. Neural Networks.

[3]  Mark E. Oxley,et al.  Physiologically motivated image fusion for object detection using a pulse coupled neural network , 1999, IEEE Trans. Neural Networks.

[4]  Jason M. Kinser,et al.  Finding the shortest path in the shortest time using PCNN's , 1999, IEEE Trans. Neural Networks.

[5]  Jason M. Kinser.  Foveation by a pulse-coupled neural network , 1999, IEEE Trans. Neural Networks.

[6]  Daoheng Yu,et al.  A new approach for automated image segmentation based on unit-linking PCNN , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[7]  M. L. Padgett,et al.  Pulse coupled neural networks (PCNN) and wavelets: biosensor applications , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[8]  Xiaodong Gu,et al.  Image thinning using pulse coupled neural network , 2004, Pattern Recognit. Lett..

[9]  Heggere S. Ranganath,et al.  Object detection using pulse coupled neural networks , 1999, IEEE Trans. Neural Networks.

[10]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[11]  Xiaodong Gu,et al.  Simplified PCNN and Its Periodic Solutions , 2004, ISNN.

[12]  Bogdan M. Wilamowski,et al.  Analog implementation of pulse-coupled neural networks , 1999, IEEE Trans. Neural Networks.

[13]  R. Eckhorn,et al.  High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey. , 1993, Neuroreport.

[14]  Wang Haiming,et al.  Binary Image Restoration Using Pulse Coupled Neural Network , 2001 .

[15]  Heggere S. Ranganath,et al.  Perfect image segmentation using pulse coupled neural networks , 1999, IEEE Trans. Neural Networks.

[16]  J. L. Johnson,et al.  Observation of periodic waves in a pulse-coupled neural network. , 1993, Optics letters.

[17]  R. Eckhorn,et al.  Coherent oscillations: A mechanism of feature linking in the visual cortex? , 1988, Biological Cybernetics.

[18]  M. L. Padgett,et al.  Pulse-Coupled Neurons for Image Filtering , 1999 .