## 1 Introduction  
In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence. principal component analysis; nonuniformity correction; infrared focal plane array; fixed-pattern noise  
Infrared focal plane arrays (IRFPAs) are widely utilized in night vision detection, industrial monitoring, medical imaging applications, and scientific research. However, due to its inconsistent pixel-level response, it exhibits a noticeable fixed-pattern noise (FPN), which is superimposed on the true image [1]. Furthermore, slow drifts in the infrared detectors' parameters tend to appear with variation in the transistor bias voltage and environmental conditions, making the traditional reference-based (e.g., two-point calibration [2]) nonuniformity correction (NUC) methods insufficient. So, plenty of scene-based nonuniformity correction (SBNUC) methods, which could be continuously adaptively updated according to scene information, have been developed to overcome this problem. Generally, SBNUC methods can be classified into two categories, i.e., statistical methods [3, 4, 5, 6, 7, 8, 9, 10] and registration-based methods [11, 12, 13, 14]. The constant statistics (CS) method proposed by [PERSON] and [PERSON] [3] first assumes a Gaussian distribution of incident irradiance, in which all pixels display the same mean and variance of the irradiance of a scene. [PERSON] et al. [4] originally developed the neural network (NN-NUC) method, which adaptively estimates a \"desired\" value to obtain the nonuniformity parameters. Based on the CS algorithm, the local CS (LCS) algorithm was then proposed by [PERSON] and [PERSON] [7]. On the basis of the Kalman filter, the NUC algorithm was presented by [PERSON] et al. [8]. By introducing a total variation approach, [PERSON] et al. developed a PDE-based method [9] that adaptively achieves estimations of the gain and bias. [PERSON] et al. [10] utilized the bilateral filter and presented a de-ghosting technique. For the algorithms based on registration, accurate interframe estimation of the motion is required. Some representatives are the motion-compensated average, [11] the algebraic scene-based algorithm, [12] the interframe registration-based algorithm (IRLMS), [13] and the multiframe registration-based adaptive NUC algorithm (MRA-NUC) [14]. Moreover, the unidirectional total variation model [15],weighted least squares [16], temporal filter [17] as well as spatial correlation [18] have been taken into consideration in order to gain a balance between complexity and real-time performance. Furthermore, with the development of various kinds of neural networks, methods based on deep learning have been gradually introduced into stripe removal and nonuniformity correction [19; 20; 21]. All the NUC methods face the same problems of convergence speed and \"ghosting artifacts\". In this paper, a multiframe registration-based NUC method combined with principal component analysis (PCA-NUC) is proposed to deal with the FPN. The PCA-NUC first employs a phase correlation algorithm [22] to achieve an accurate multiframe registration, and principal component analysis is introduced to eliminate the noise pattern and finally construct the desired output.