  In this paper, we present on-sensor neuromorphic vision hardware
implementation of denoising spatial filter. The mean or median spatial filters
with fixed window shape are known for its denoising ability, however, have the
drawback of blurring the object edges. The effect of blurring increases with an
increase in window size. To preserve the edge information, we propose an
adaptive spatial filter that uses neuron's ability to detect similar pixels and
calculates the mean. The analog input differences of neighborhood pixels are
converted to the chain of pulses with voltage controlled oscillator and applied
as neuron input. When the input pulses charge the neuron to equal or greater
level than its threshold, the neuron will fire, and pixels are identified as
similar. The sequence of the neuron's responses for pixels is stored in the
serial-in-parallel-out shift register. The outputs of shift registers are used
as input to the selector switches of an averaging circuit making this an
adaptive mean operation resulting in an edge preserving mean filter. System
level simulation of the hardware is conducted using 150 images from Caltech
database with added Gaussian noise to test the robustness of edge-preserving
and denoising ability of the proposed filter. Threshold values of the hardware
neuron were adjusted so that the proposed edge-preserving spatial filter
achieves optimal performance in terms of PSNR and MSE, and these results
outperforms that of the conventional mean and median filters.
