  Purpose- High speed image processing is a challenging task for real-time
applications such as product quality control of manufacturing lines. Smart
image sensors use an array of in-pixel processors to facilitate high-speed
real-time image processing. These sensors are usually used to perform the
initial low-level bulk image filtering and enhancement. Design- In this paper,
using pulse-width modulated signals and regular nearest neighbor
interconnections, a convolution image processor is presented. The presented
processor is not only capable of processing arbitrary size kernels, but the
kernel coefficients can be any arbitrary positive or negative floating number.
Findings- The performance of the proposed architecture is evaluated on a FPGA
platform. The peak signal-to-noise ratio (PSNR) metric is used to measure the
computation error for different images, filters, and illuminations. Finally,
the power consumption of the circuit in different operation conditions is
presented. Originality/Value- The presented processor array can be used for
high speed kernel convolution image processing tasks including arbitrary size
edge detection and sharpening functions which require negative and fractional
kernel values.
