With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors
are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging
techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity
correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point
correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are
limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of
these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and
another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is
introduced, and involves the compare result between improved neural networks and other adaptive correction techniques.
A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware
implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm
offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides
better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful
in future infrared imaging systems.