# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 1 Introduction  
Space-based infrared point target (IRPT) detection is one of the most important applications of the on-board intelligent information processing technology, which has been wildly used in both the civil and military fields. Blind pixel suppression is the essential preprocess for all infrared camera applications. Owing to the limitation of the material, craftwork, and working environment, some infrared pixels will experience permanent or temporary functional degradation, namely blind pixels. Especially, blind pixels with a visual feature similar to that of IRPT can easily lead to false detection of IRPT and obstruct the real-time performance of the IRPT detection algorithm. Indeed, threshold filter [1], coding mechanism [2], and support vector machine [3], etc., are proposed to eliminate false alarm sources and have obtained good results in the IRPT detection algorithm. However, these sophisticated methods consume most computing resources to eliminate more indistinguishable false alarm sources from background. These blind pixels have a catastrophic effect on on-board intelligent information processing if they are not properly suppressed. Therefore, an effective blind pixel suppression method is vitally needed for on-board IRPT detection application. Generally, blind pixels are divided into constant blind pixels and flickering pixels [4, 5]. The constant blind pixels which appear as loop-live over-bright or over-dark can be corrected by the existing method effectively. However, considering that the flickering pixels can oscillate randomly between several levels, flickering pixels suppression is a hardtask. Nowadays, blind pixel suppression is divided into two steps: blind pixel detection and blind pixel compensation. The blind pixel detection method can be classified into two categories. The first category is the calibration-based method [6; 7; 8], which uses the blackbody to obtain uniform reference images and detect blind pixels by comparing each pixels' response rate, deviation factor, noise statistics, and other properties. This method is easy to implement and has a good detection effect for constant blind pixels. However, flickering pixels cannot be detected accurately by this method due to their randomicity. More importantly, this method requires additional hardware equipment, such as blackbody and electromechanical parts, which will consume valuable hardware resources of satellites. The other category is the scene-based blind pixel detection method, which utilizes image processing and computer vision to statistically analyze the scene image. Although this method has a lower accuracy compared to the calibration-based method, it can operate during the system's normal mission and does not require other equipment. Namely, this method has better real-time performance and adaptability. Presently, one of the most important tools for scene-based blind pixel detection methods is image registration. [PERSON] et al. [9] proposed a method that created bad pixel masks by frame registration using simple pixel differences, and produced good results for detecting moving, low-contrast targets at the same time. [PERSON] et al. [10] adopted a registration-based method that could correct the fixed pattern noise with less error using the estimation of scene motion. In addition, [PERSON] et al. [11] used the correlated phase information to make the registration more precise and adopted linear mapping to estimate true pixel value. However, the efficiency of image registration methods may influence the real-time performance of these methods. For instance, the registration based on the Fourier transform would make some methods difficult to apply to real-time hardware [11]. In addition, a good image registration effect asks for rich scene details, which limits the application for some scenarios. Recently, analyzing the intensity of adjacent pixels is also efficient. [PERSON] and [PERSON] [12] proposed real-time and online defective pixel detection and correction algorithms.