School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Institute of Information Science and Engineering, Changji Vocational and Technical College, Changji 831100, China.
Sensors (Basel). 2022 Apr 25;22(9):3277. doi: 10.3390/s22093277.
Aiming at a thorny issue, that conventional small target detection algorithm using local contrast method is not sensitive for residual background clutter, robustness of algorithms is not strong. A Gaussian fusion algorithm using multi-scale regional patch structure difference and Regional Brightness Level Measurement is proposed. Firstly, Regional Energy Cosine (REC) is constructed to measure the structural discrepancy among a small target with neighboring cells. At the same time, Regional Brightness Level Measurement (RBLM) is constructed utilizing the brightness difference characteristics between small target and background areas. Then, a brand new Gaussian fusion algorithm is proposed for the generated saliency map in multi-scale space to characterize the overall heterogeneity in original infrared small target and local neighborhood. Finally, a self-adapting separation algorithm is adopted with the objective to obtain a small target from background interference. This method is able to utmostly restrain background interference and enhance the target. Extensive qualitative and quantitative testing results display that the desired algorithm has remarkable performance in strengthening target region and restraining background interference compared with current algorithms.
针对传统的基于局部对比度的小目标检测算法对残余背景杂波不敏感、算法鲁棒性不强的问题，提出了一种基于多尺度区域斑块结构差异和区域亮度水平测量的高斯融合算法。首先，构建区域能量余弦（REC）来测量小目标与相邻单元之间的结构差异。同时，利用小目标与背景区域之间的亮度差特征，构建区域亮度水平测量（RBLM）。然后，在多尺度空间中生成的显著图上提出了一种新的高斯融合算法，以描述原始红外小目标和局部邻域的整体异质性。最后，采用自适应分离算法，从背景干扰中获取小目标。该方法能够最大限度地抑制背景干扰，增强目标。广泛的定性和定量测试结果表明，与现有算法相比，所提出的算法在增强目标区域和抑制背景干扰方面具有显著的性能。
