红外图像场景非均匀性校正的鬼影抑制
摘要
Scene-based adaptive nonuniformity correction (NUC) is currently being applied to achieve higher performance in infrared imaging systems. However, almost all scene-based NUC algorithms cause the production of ghosting artifacts over output images. Based on constant-statistics theory, we propose a novel threshold self-adaptive ghosting reduction algorithm to improve the space low-pass and temporal high-pass (SLP-THP) NUC technique. The correction parameters of the previous frame are regarded as thresholds to compute new correction parameters. Experimental results show that the proposed algorithm can obtain a satisfactory performance in reducing unwanted ghosting artifacts.
引用本文（GB/T 7714）
Junqi Bai, Qian Chen, Weixian Qian, 等. 红外图像场景非均匀性校正的鬼影抑制[J]. Chinese Optics Letters, 2010.
引文网络
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