Blind-pixel correction algorithm for an infrared focal plane array based on moving-scene analysis

Wang, Bingjian; Shang-qian, Liu; Li, Qing; Lei, Rui

doi:10.1117/1.2179775

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…For one thing, the small target is relatively small, roughly several pixels 1,2 ; for the other thing, the gray value distribution is different from surrounding local background, that is, the target has no spatial correlation with the surroundings. [3][4][5][6] Thus, a class of background estimation methods that utilize gray value differences between the small target and its surroundings is proposed. For example, Chen et al proposed a local contrast method (LCM) 3 and kernel regression model, [7][8][9] which use local gray value contrast of small target and its surroundings to distinguish the target from background.…”

Section: Introductionmentioning

confidence: 99%

“…So we can obtain an improved local adaptive contrast measure (ILACM) by calculating the summation of the Euclidean distance between a center patch and its surrounding areas, which could successfully suppress the pixel-size electronic noises (PSENs) with high brightness. 6 Then, ILACM is used to measure the patch similarity among different patches. At last, we utilize the patch with maximum patch similarity to estimate the background image in order to preserve more edges in the background image.…”

Section: Introductionmentioning

confidence: 99%

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Small target detection using edge-preserving background estimation based on maximum patch similarity

Wang

Yue

et al. 2017

International Journal of Advanced Robotic Systems

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Infrared small target detection is widely applied in lots of practical applications, but due to the complicated edges in practical scenarios, most existing detection algorithms usually lead to many false alarms and cannot detect the target accurately. Addressing this problem, a novel edge-preserving background estimation method based on maximum patch similarity was proposed in this article. At first, we will propose an improved local adaptive contrast measure to suppress the pixel-size electronic noises. Then, maximum patch similarity with minimum improved local adaptive contrast measure can be utilized to preserve the edge in the estimated background. Finally, we can obtain target image by filtering the background image from original image and use adaptive threshold segmentation to detect the small target in our target image. It is shown from experiments that our proposed method has better detection results in diverse infrared images, improving signal-to-clutter ratio gain and background suppression factor of the images significantly and efficiently.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small target detection using edge-preserving background estimation based on maximum patch similarity

Wang

Yue

et al. 2017

International Journal of Advanced Robotic Systems

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“…One of the essential steps for bad pixel replacement is to compensate for bad pixels. There are two ways to realize bad pixel replacement: bad pixel correction based on moving-scene analysis [2] and adaptive bad pixel replacement. The former uses moving-scene analysis to calculate movement parameters estimated from an image sequence.…”

Section: Introductionmentioning

confidence: 99%

Bad pixel replacement based on spatial statistics for IR sensor

et al. 2010

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IR focal plane arrays typically contain bad pixels. Bad pixels have to be corrected because those can significantly impair the performance of target-detection algorithms. On the other hand, particularly as an aid to visual interpretation, it is desirable to replace the bad pixels. IR image contains spatial information and is correlative in spatial domain. In spatial statistics the semivariogram is an important function that relates semivariance to sampling lag. This function can characterize the spatial dependence of each point on its neighbor and provide a concise and unbiased description of the scale and pattern of spatial variability. One of the main reasons for deriving semivariogram is to use it in the process of estimation. Kriging is an interpolation and estimation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas. In this paper a new technique based on spatial statistics is developed for bad pixel replacement. The main objective of the technique is to replace bad pixels through Kriging estimation. Theory analysis and experiments show that the method is reasonable and efficient.

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“…Meanwhile, due to the IR focal plane array material properties, as well as the electronic noises, there are usually many pixelsized noises with high brightness (PNHB) in IR images [2], which are similar to targets and introduce a high false alarm rate. In addition, real-time target output is needed in many applications [3]; thus, detection algorithms with fast speed are much more popular.…”

Section: Introductionmentioning

confidence: 99%

A Robust Infrared Small Target Detection Algorithm Based on Human Visual System

Han

Zhang

et al. 2014

IEEE Geosci. Remote Sensing Lett.

360

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Robust human visual system (HVS) properties can effectively improve the infrared (IR) small target detection capabilities, such as detection rate, false alarm rate, speed, etc. However, current algorithms based on HVS usually improve one or two of the aforementioned detection capabilities while sacrificing the others. In this letter, a robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously. First, an HVS size-adaptation process is used, and the IR image after preprocessing is divided into subblocks to improve detection speed. Then, based on HVS contrast mechanism, the improved local contrast measure, which can improve detection rate and reduce false alarm rate, is proposed to calculate the saliency map, and a threshold operation along with a rapid traversal mechanism based on HVS attention shift mechanism is used to get the target subblocks quickly. Experimental results show the proposed algorithm has good robustness and efficiency for real IR small target detection applications.Index Terms-Human visual system (HVS), improved local contrast measure (ILCM), infrared (IR) small target.

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Blind-pixel correction algorithm for an infrared focal plane array based on moving-scene analysis

Cited by 11 publications

References 3 publications

Small target detection using edge-preserving background estimation based on maximum patch similarity

Small target detection using edge-preserving background estimation based on maximum patch similarity

Bad pixel replacement based on spatial statistics for IR sensor

A Robust Infrared Small Target Detection Algorithm Based on Human Visual System

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