Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed pattern noise (FPN) and seriously affects the image quality of the infrared imaging system. This paper proposed a novel scene-based non-uniformity correction method with a new edge-preserve filter and adaptive learning rate. First, using co-occurrence filter as the desired image estimation, the proposed method removed the FPN while preserving the image details. Then, an adaptive learning rate connected with both temporal motion and spatial correlation factor is utilized to decrease the effect of ghosting artifacts. In this way, the proposed method overcomes the shortcomings of the traditional scene-based non-uniformity. Several real infrared image sequences collected in different conditions are used to verify the performance of the proposed method. The experimental results demonstrate that the proposed method has a much better visual effect, making a great balance between the non-uniformity correction and details preservation. Compared with other good NUC methods, this method also has better performance in the aspects of applicability and robustness, which has great application value.INDEX TERMS Non-uniformity correction, IRFPA, edge-preserve filter, adaptive learning rate, ghosting artifacts, details preservation.
Abstract. In this paper, the noises of images taken by Cannon40D and CMOS industrial camera are analyzed both in time and space domain without outside illumination. The dark random noise associated with time is very low, which means the system noise is almost uncorrelated with time. However, system noise in the center of the CMOS image sensor is lower than those in the marginal area, which suggests the noise presents a certain spatial distribution. Finally, taking the temperature effect into consideration, we find the relationship between the maximum noise and temperature.
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