Infrared image quality is a key factor of object detection. Stripe nonuniformity is very typical in the staring infrared focal plane array (IRFPA). In this paper, we propose a novel high-space-frequency correction method to eliminate the stripe nonuniformity. The kernel ideal is to eliminate the high-space-frequency part of stripe nonuniformity and retain its low-space-frequency part which can reduce unwanted ghosting artifacts. Firstly, the spatial characteristic of stripe nonuniformity is discussed, then correction parameters are computed based on the spatial high frequency part of image. Experimental results show the proposed mehtod can compute adaptive correction parameters of each readout channel and obtain a reliable stripe nonuniformity reduction.
Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
For infrared focal plane array sensors, imagery is degraded by a number of phenomena during signal acquisition, particularly including under-sampling and detector non-uniformity. In this paper, we propose an efficient framework which combines neural network non-uniformity correction with image registration for removing structured and non-structured noise and increasing spatial resolution. To achieve this, we sequentially improve the image quality in two steps: primarily, removing the structured and non-structured noise based on neural network theory, and achieving registration using an iterative gradient-based registration technique. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm. By using our method, the shifts between acquired frames are estimated precisely and the quality of reconstructed image is improved.