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基于多尺度峭度图融合和光流法的红外小目标检测。

Infrared Small Target Detection Based on Multiscale Kurtosis Map Fusion and Optical Flow Method.

机构信息

School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China.

出版信息

Sensors (Basel). 2023 Feb 2;23(3):1660. doi: 10.3390/s23031660.

Abstract

The uncertainty of target sizes and the complexity of backgrounds are the main reasons for the poor detection performance of small infrared targets. Focusing on this issue, this paper presents a robust and accurate algorithm that combines multiscale kurtosis map fusion and the optical flow method for the detection of small infrared targets in complex natural scenes. The paper has made three main contributions: First, it proposes a structure for infrared small target detection technology based on multiscale kurtosis maps and optical flow fields, which can well represent the shape, size and motion information of the target and is advantageous to the enhancement of the target and the suppression of the background. Second, a strategy of multi-scale kurtosis map fusion is presented to match the shape and the size of the small target, which can effectively enhance small targets with different sizes as well as suppress the highlighted noise points and the residual background edges. During the fusion process, a novel weighting mechanism is proposed to fuse different scale kurtosis maps, by means of which the scale that matches the true target is effectively enhanced. Third, an improved optical flow method is utilized to further suppress the nontarget residual clutter that cannot be completely removed by multiscale kurtosis map fusion. By means of the scale confidence parameter obtained during the multiscale kurtosis map fusion step, the optical flow method can select the optimal neighborhood that matches best to the target size and shape, which can effectively improve the integrity of the detection target and the ability to suppress residual clutter. As a result, the proposed method achieves a superior performance. Experimental results on eleven typical complex infrared natural scenes show that, compared with seven state-of-the-art methods, the presented method outperforms in terms of subjective visual effect, as well as some main objective evaluation indicators such as BSF, SCRG and ROC, etc.

摘要

小尺寸目标的尺寸不确定性和背景复杂性是导致小红外目标检测性能较差的主要原因。针对这一问题,本文提出了一种稳健、准确的算法,该算法结合多尺度峰度图融合和光流法,用于检测复杂自然场景中的小红外目标。本文主要有三个贡献:首先,提出了一种基于多尺度峰度图和光流场的红外小目标检测技术结构,该结构能够很好地表示目标的形状、大小和运动信息,有利于目标的增强和背景的抑制。其次,提出了一种多尺度峰度图融合策略来匹配小目标的形状和大小,可以有效地增强不同大小的小目标,同时抑制高亮噪声点和残留背景边缘。在融合过程中,提出了一种新的加权机制来融合不同尺度的峰度图,通过该机制可以有效地增强与真实目标匹配的尺度。第三,利用改进的光流法进一步抑制多尺度峰度图融合无法完全去除的非目标残留杂波。通过多尺度峰度图融合步骤中获得的尺度置信参数,光流法可以选择与目标大小和形状匹配最佳的最优邻域,从而有效提高检测目标的完整性和抑制残留杂波的能力。因此,所提出的方法具有优越的性能。在十一个典型的复杂红外自然场景上的实验结果表明,与七种最先进的方法相比,所提出的方法在主观视觉效果以及 BSF、SCRG 和 ROC 等一些主要客观评价指标方面都具有更好的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4446/9921381/a45216290d44/sensors-23-01660-g001.jpg

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