4 March 1996 Fuzzy cellular neural network for image enhancement
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Cellular neural networks (CNNs) are currently being used to arrive at solutions to the problems in image processing and pattern recognition. In this paper a technique for image enhancement using a fuzzy CNN is proposed. This technique exploits the massive parallelism of CNNs and mathematical framework of fuzzy logic to cope respectively with the computational complexity and uncertainty in noisy image. The mathematical model of discrete time cellular neural network (DTCNN) is obtained from the circuit equations of a cell. An architecture of fuzzy CNN for image enhancement is proposed. The network extracts the original image from a given noisy image by self organization. The fuzziness of the output image at each iteration is taken as a measure of error, which is in turn used to adapt the input image. An algorithm for adaptation of input image for linear and quadratic indices of fuzziness is derived. The efficacy of the proposed technique is verified through simulation results. Tests are carried out on noisy images obtained by adding zero mean Gaussian noise to synthetic bitonic images. The application of the proposed network for enhancement of noisy images with different noise levels is demonstrated.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dandina Hulikunta Rao, Dandina Hulikunta Rao, P. I. Hosur, P. I. Hosur, } "Fuzzy cellular neural network for image enhancement", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234264; https://doi.org/10.1117/12.234264

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