International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 1 (2006)

 


A high performance architecture for color image enhancement using 

a machine learning approach


Ming Z. Zhang, Ming-Jung Seow, Vijayan K. Asari
Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA


Abstract
A novel architecture for performing color image enhancement using a machine learning algorithm called Ratio Rule is proposed in this paper. The approach promotes log-domain computation to eliminate all multiplications and divisions, utilizing the approximation techniques for efficient estimation of the log2 and inverse-log2. A new quadrant symmetric architecture is also presented to provide very high throughput rate for homomorphic filters which is part of the pixel intensity enhancement across RGB components in the system. The pipelined design of the filter features the flexibility in reloading a wide range of kernels for different frequency responses. A new approach for the design of the uniform filters is also presented to reduce the processing element arrays (PEAs) from W PEAs to 2 PEAs for W×W window. This new concept is applied to assist in training the synaptic weights of the neural network for color balancing to restore the intensity enhanced image to its natural color existed in original image. It is observed that the performance of the system with parallel pipelined architectures is able to achieve 147.3 million outputs per second (MOPS), or equivalently 57.9 billion operations per second on Xilinx’s Virtex II XC2V2000-4ff896 FPGA at a clock frequency of 147.3 MHz.

Keywords
color image enhancement, Ratio Rule, log-domain computation, quadrant symmetric architecture, uniform filter, parallel pipelined architecture.

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