Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes

TL;DR

This paper presents a parallel software implementation of recursive multidimensional digital filters designed for point-target detection in cluttered infrared scenes, utilizing CPU and GPU architectures to enhance processing efficiency.

Contribution

It introduces a novel parallel implementation of recursive 3-D spectrum estimation and filtering techniques for improved infrared target detection.

Findings

Effective background clutter subtraction demonstrated

Parallel CPU and GPU implementations show significant speedup

Enhanced detection of point targets in infrared imagery

Abstract

A technique for the enhancement of point targets in clutter is described. The local 3-D spectrum at each pixel is estimated recursively. An optical flow-field for the textured background is then generated using the 3-D autocorrelation function and the local velocity estimates are used to apply high-pass velocity-selective spatiotemporal filters, with finite impulse responses (FIRs), to subtract the background clutter signal, leaving the foreground target signal, plus noise. Parallel software implementations using a multicore central processing unit (CPU) and a graphical processing unit (GPU) are investigated.