Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

A Local Contrast Method for Small Infrared Target Detection

Multiscale patch-based contrast measure for small infrared target detection

Against an intricate infrared cloudy-sky background, jamming objects such as the edges of clouds in the scene have a similar thermal intensity measure with respect to the background as small targets. This may cause high false alarm rates and low probabilities of detection according to conventional small target detection methods. In this paper, we propose a weighted local difference measure (WLDM)-based scheme for the detection of small targets against various complex cloudy-sky backgrounds. Initially, a WLDM map is achieved to simultaneously enhance targets and suppress background clutters and noise. In this way, the true targets can be easily separated from jamming objects. After that, a simple adaptive threshold is used to segment the targets. More than 460 infrared small target images against diverse intricate cloudy-sky backgrounds were utilized to validate the detection capability of the WLDM-based method. Experimental results demonstrate that the proposed algorithm not only works more robustly for different cloudy-sky backgrounds, target movements, and signal-to-clutter ratio (SCR) values but also has a better performance with regard to the detection accuracy, in comparison to traditional baseline methods. In particular, the proposed method is able to significantly improve SCR values of the images.

Small Infrared Target Detection Based on Weighted Local Difference Measure

Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track

We propose an effective small-target detection approach based on weighted image entropy. The approach weights the local entropy measure by the multiscale grayscale difference followed by an adaptive threshold operation, which aims to improve the signal-to-noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target. The detection capability of the proposed approach has been validated on six real sequences, and the results demonstrate its significance and improvement.

Infrared small-target detection using multiscale gray difference weighted image entropy

According to the principle of human discrimination of small targets from a natural scene that there is a signature of discontinuity between the object and its neighboring regions, we develop an efficient method for multiscale small target detection using template matching based on a dissimilarity measure, which is called an average gray absolute difference maximum map (AGADMM), and infer the criterion of recognizing multiscale small objects from the properties of the AGADMM of the natural scene, which is a spatially independent and stable Gaussian random field. We explain how the AGADMM increases the ratio of the signal of object-to-background perturbations, improves the detectable probability, and keeps the false alarm probability very low. We analyze the complexity of computing an AGADMM and justify the validity and efficiency. Experiments with images of a natural scene such as a sky and sea surface have shown the great potential of the proposed method for distinguishing multiscale small objects from a natural scene.

Efficient method for multiscale small target detection from a natural scene

According to the principle of human discrimination of a small object from a natural scene in which there is the signature of discontinuity between the object and its neighbor regions, we develop an efficient algorithm for small object detection based on template matching by using a dissimilarity measure that is called average gray absolute difference maximum map (AGADMM), infer the criterion of recognizing a small object from the properties of the AGADMM of a natural scene that is a spatially independent and stable Gaussian random field, explain how the AGADMM improves the detectable probability and keeps the false alarm probability very low, analyze the complexity of computing AGADMM, and justify the validity and efficiency. Experiments with visual images of a natural scene such as sky and sea surface have shown the great potentials of the proposed method for distinguishing a small man-made object from natural scenes.

Efficient small target detection algorithm

A method that makes the Hopfield neural network perform the point pattern relaxation matching process is proposed. An advantage of this is that the relaxation matching process can be performed in real time with the massively parallel capability to process information of the neural network. Experimental results with large simulated images prove the effectiveness and feasibility to perform point relaxation matching by the Hopfield neural network.

Luogang Wei

Papers

Efficient method for multiscale small target detection from a natural scene

Efficient small target detection algorithm

Relaxation matching by the Hopfield neural network