Adaptive detection for infrared small target under sea-sky complex background

Yang, Lei; Yang, Jie; Yang, Kai

doi:10.1049/el:20045204

Cited by 201 publications

(100 citation statements)

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“…Here, we employ it in the following experiments for measuring the effect of noise suppression of the enhanced image. Further, the calculation of Ω is introduced below: [42] characterizing the information amount contained in an enhanced image is employed to measure the degree of over-enhancement in our experiment. Additionally, DE is a globally statistical index which is defined as:…”

Section: Comparison Of Processed Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan

Qian

et al. 2018

Remote Sensing

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Abstract:Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

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Section: Comparison Of Processed Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan

Qian

et al. 2018

Remote Sensing

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“…where χ k)∈[0,1] is the bin value for an arbitrary gray level k (k = 1, 2, 3, …, L and L is the maximum gray level value) in the saliency histogram; sum(χ) means the sum of all the bin values which is used to make a normalization; N k) = NUM{(x,y)|I x,y = k} refers to the total number of the pixels whose gray levels are k in I, where NUM{·} means counting the number of elements in a set; S a (k) is defined as the cumulative sum of a set of saliency values in S, and S a k) = S x,y , x,y ∈{(x,y)|I x,y = k} y x (7) Now, let us have a further discussion for the design of our saliency histogram. First of all, the ideal result of saliency histogram should meet the request that only those bins whose gray levels correspond to IR target are assigned with large saliency values.…”

Section: Saliency Histogrammentioning

confidence: 99%

“…To efficiently examine small moving targets and remove various sorts of background clutters in IR images simultaneously, numerous algorithms have been developed so far, including filter based methods, mathematical morphology based methods, wavelet based methods, and so on. Filter based methods, the representatives of which are max-mean/max-median filter [6], high-pass filter [7] as well as two-dimensional least mean square (TDLMS) filter [8], utilize fixed templates to suppress clutters according to intensity difference. Although they can meet the requirement of real-time processing, the results are always inaccurate [9,10].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Wan

Ren

et al. 2017

Applied Sciences

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Abstract:In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

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“…According to the different angles, the detection algorithm can have different classification methods: the different information used can be divided into direct method and indirect method; the detection and tracking of sequence can be divided into different detections before tracking method (DBT) and track before detect method (TBD); and also a classification method of spatial filtering and time filtering and static background and moving background classification method [21]. The following classifications according to the direct method and indirect method are introduced and a brief analysis of the existing detection algorithms is conducted in this paper [22].…”

Section: Current Situation Of Infrared Small Target Detectionmentioning

confidence: 99%

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Yang¹

2015

TOAUTOCJ

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Infrared dim target detection and tracking is widely used in precision guidance, automatic control and other fields. As the small target imaging system has far distance, small area, low SNR infrared characteristics and undergoes image preprocessing, dim target detection technology is more important and more difficult. As a full play of the advantages of infrared small target detection technique has been provided, this has significance in infrared imaging. Based on introducing the tracking technology development status at home and abroad by the study of small target detection, the problems of detecting and tracking small moving target in infrared image sequence of background noise and interference conditions, aiming at dynamic planning in energy diffusion problem are presented. The improved algorithm of dynamic programming is also presented including weighted dynamic programming algorithm and the realization steps of the algorithm. Detection structure and the experimental results are analyzed. The results show that the algorithm has a good effect. In the end, this paper studies the rejected false targets in IR image track association method to obtain real object trajectories for moving small target detection, so as to improve the detection rate and reduce the false alarm rate.

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Adaptive detection for infrared small target under sea-sky complex background

Cited by 201 publications

References 0 publications

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

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