Objective

2

Infrared dim and small target detection is a research interest in the field of infrared image processing

which is difficult but practical. It plays a crucial role in reconnaissance and warning

aircraft tracking

and missile guidance systems. The process of detecting infrared small targets in natural scenes is characterized by the fact that the target area can frequently be expressed as a small

uniform

compact area with a significant discontinuity or contrast compared with the surrounding background. The detection of a small target in an infrared image is affected by many factors

such as the small number of target pixels

low contrast between a target and a background

dim edges of the targets

complex image background

and lack of texture information of the small targets

thereby resulting in the difficulty of infrared small target detections. The existing methods have achieved effective results in detecting small targets in infrared images; however

drawbacks

such as low adaptability to complex background

low detection rate

and high false alarm rate

still remain. In addition

methods related to sparse representation have the following shortcomings:the construction of a dictionary directly from the original images ignores the feature extraction of the target

or does not establish the target and the background dictionaries simultaneously

thus resulting in a weak representation capability of the entire dictionary. Thus

an infrared small target detection algorithm that combines facet directional derivative features with sparse representation is proposed.

Method

2

A dictionary must initially be constructed. A background dictionary is constructed by intercepting 1 000 small blocks and then obtaining their derivatives in a certain direction. K-SVD algorithm is used to train the blocks after merging them into column vectors. A background dictionary with 500 atoms is achieved. The construction method of the target dictionary is as follows:325 small blocks containing small targets are generated in accordance with the characteristics of the small target. The first-order derivative in one direction is calculated for these small blocks containing small targets

and then the columns are converted into column vectors. The target dictionary containing 325 atoms is obtained in that direction after normalizing. We combine the target and the background dictionaries into one large dictionary with 825 atoms

which will be used in the subsequent sparse solution section. The facet model is utilized to extract the first-order derivative features of the original infrared image in four directions

that is 0°

90°

45°

and -45°. Then

the blocks separated from the image are processed from top to bottom and left to right on the basis of the directional derivative information through the sparse representation method. The detection result map is constructed using the sparse coefficients and reconstruction residuals of the derivative image blocks. Finally

a threshold is calculated from the detection result map to separate the target from the background.

Result

2

The classical max-mean and max-median algorithms are selected as the algorithms for comparison. Comparative results show that the max-mean and max-median algorithms are sensitive to the edges in the infrared image. The traditional algorithms perform ineffectively in removing these clusters when the infrared image has clusters due to distance

atmospheric refraction

lens aberration

and optical defocus. A 3D image of the detection result shows that our method has better performance

is insensitive to noise

and can achieve an excellent target detection effect. Therefore

our algorithm has certain advantages over the traditional algorithms. Receiver operating characteristic (ROC) curves of detection and false alarm rates are plotted through experimental verification of four infrared image sequences. The results evidently show that the proposed algorithm has a higher detection rate and lower false alarm rate in a small target detection than other algorithms.

Conclusion

2

Our algorithm extracts image directional derivative information through the facet model

combines the directional derivative features of infrared imagery with sparse representation theory

analyzes the characteristics of the small target in a single direction in detail

and extends it to feature information presented in multiple directions. The difference between the target and the background is discussed. The final test results of the small target are obtained using sparse representation theory as a medium. Experiments show that the proposed algorithm has a high detection accuracy and strong anti-noise capability. The proposed algorithm has certain advantages

improves detection rate

and reduces false alarm rate over the traditional detection algorithms. Another important advantage of our algorithm is that it can generate different background dictionaries in accordance with a certain background under different conditions to obtain improved detection results and perform an effective pertinence in practical applications.