Adaptive image sensing and enhancement using the cellular neural network universal machine

As an attempt to introduce interactive, content-dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN-UM sensor-computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on-line or off-line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN-UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN-UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi-layer, complex cell CNN architecture. In (Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-2000) Catania, 2000; 213–217) 3-layer architecture is described which is capable of realizing the main part of the second enhancement method. The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN-UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium-computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.

[1]  A. Rodriguez-Vazquez,et al.  A 64/spl times/64 CNN universal chip with analog and digital I/O , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[2]  Jian Fan,et al.  Mammographic feature enhancement by multiscale analysis , 1994, IEEE Trans. Medical Imaging.

[3]  Michael J. Berry,et al.  Adaptation of retinal processing to image contrast and spatial scale , 1997, Nature.

[4]  L. Álvarez,et al.  Signal and image restoration using shock filters and anisotropic diffusion , 1994 .

[5]  Triestevia A. Valerio.  A Rational Unsharp Masking Technique , 1998 .

[6]  Valeri Mladenov,et al.  Cellular Neural Networks: Theory And Applications , 2004 .

[7]  Tamás Roska,et al.  Analogic CNN computing: architectural, implementation, and algorithmic advances-a review , 1998, 1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359).

[8]  Tamás Roska,et al.  Adaptive histogram equalization with cellular neural networks , 1996, 1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96).

[9]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[10]  Tamás Roska.  Computer-Sensors: Spatial-Temporal Computers for Analog Array Signals, Dynamically Integrated with Sensors , 1999, J. VLSI Signal Process..

[11]  Jessica K. Hodgins,et al.  Two methods for display of high contrast images , 1999, TOGS.

[12]  Frank S. Werblin,et al.  The computational eye , 1996 .

[13]  Leon O. Chua,et al.  The analogic cellular neural network as a bionic eye , 1995, Int. J. Circuit Theory Appl..

[14]  Leon O. Chua,et al.  Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial , 1995 .

[15]  Dennis M. Healy,et al.  Contrast enhancement of medical images using multiscale edge representation , 1994, Defense, Security, and Sensing.

[16]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[17]  Hui Zhu,et al.  Image Contrast Enhancement by Constrained Local Histogram Equalization , 1999, Comput. Vis. Image Underst..

[18]  Ákos Zarándy,et al.  CNN-based models for color vision and visual illusions , 1999 .

[19]  A. Rodriguez-Vazquez,et al.  CNNUC3: a mixed-signal 64/spl times/64 CNN universal chip , 1999, Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems.

[20]  Giovanni Ramponi,et al.  Rational unsharp masking technique , 1998, J. Electronic Imaging.

[21]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[22]  T. Serrano-Gotarredona,et al.  A stored program 2/sup nd/ order/3-layer complex cell CNN-UM , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[23]  Patrenahalli M. Narendra,et al.  Real-Time Adaptive Contrast Enhancement , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .