A Hardware Architecture for Color Image Enhancement Using a Machine Learning Approach with Adaptive Parameterization

A novel architecture for performing color image enhancement using a machine learning algorithm called Ratio Rule is proposed in this paper. The threshold width of the activation function is automatically determined from the image characteristics. The approach promotes log-domain computation to eliminate all multiplications and divisions, utilizing approximation techniques for efficient estimation of the log2 and inverse-log2. The design incorporates the dynamic thresholds of the activation functions and update rate. The improved 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 times 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 the original image. The concept of uniform filter is further extended to design max/min filters. It is observed that the performance of the system with parallel and pipelined architectures is able to achieve 139.3 million outputs per second (MOPS), or equivalently 54.7 billion operations per second on Xilinx´s Virtex II XC2V2000-4ff896 FPGA at a clock frequency of 139.3 MHz.