Adaptive neural network non-uniformity correction based on edge detection and running on hardware

The Fixed Pattern Noise (FPN) of the infrared focal plane array severely limits the system performance, and the non-uniformity correction algorithm is a key technique of thermal imaging system. The scene-based non-uniformity correction algorithm does not require a shutter to block the field of view, but utilizes the scene information of image sequences to calculate the infrared focal plane array non-uniformity parameters. This paper introduces an improved neural network non-uniformity correction algorithm, which speeds up the convergence rate of the conventional neural network algorithm. The improved algorithm employs the edge detection method to overcome the ghosting artifacts generated by the conventional algorithm. The algorithm has run on a small low power consumption DSP hardware platform with TMS320DM643 as the kernel processor and can do the correction in a simple way with satisfactory results, so the algorithm introduced in this paper is proved to be reasonable and effective.