Abstract
In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.
Highlights
Dim and small target signal detection is of great importance for high altitude warning [1,2]
When the gradient difference between the inner and outer windows is compared with the preset threshold Th, if the gradient difference between the inner and outer windows is greater than the preset Th, the region is regarded as the significant region, and the significant region block is subjected to high-order motion correlation by using the motion correlation of the target between adjacent frames
Since the signal-to-noise of the target in the image is still relatively low even after spiral gradient estimation under strong daylight illumination, which is not conducive to target detection and extraction, this paper proposes an improved higher-order correlation energy accumulation model to enhance the image target signal, which mainly uses the motion correlation of the target between adjacent frames to perform higher-order motion correlation for significant blocks of regions
Summary
Dim and small target signal detection is of great importance for high altitude warning [1,2]. Using the motion characteristics of target to detect and extract real target through adaptive threshold segmentation, and the effect of background modeling in low altitude and long-distance scene is better This paper proposes a spiral gradient optimization estimation of dim signal detection algorithm, based on the pre-designed spiral motion model for perturbation to obtain local gradient information in a neighborhood of an image element, and rely on the gradient optimization model to estimate the local optimal background to achieve background suppression while effectively removing the center of the halo, which greatly improves the detection ability of subsequent targets. A gradient-optimized background estimation model is constructed as follows:
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