Infrared Small Target Detection Based on Background Estimation and Scale Fusion

Local contrast methods for infrared small target detection techniques have attracted much attention. However, for some small targets with obvious contours and large scales, the multiscale strategy fails to achieve the expected results. To address this problem, this letter proposes an infrared small target detection method based on background estimation and scale fusion. First, a background estimation filter is proposed based on the idea of background estimation, which is combined with a ratio local contrast to design a ratio-estimated local contrast measurement (RELCM) to initially capture the target while suppressing low-frequency clutter. Second, a single-scale and multiscale differential contrast measurement (SMDCM) is proposed by combining single-scale intrafeatures with multiscale interfeatures to further suppress clutter while enhancing the target. Combine RELCM with SMDCM to obtain the ratio estimation scale fusion local contrast measurement (RESFLCM). Finally, an adaptive thresholding operation is used to extract the final target. Extensive experimental results show that the RESFLCM performs better detecting small targets of different scales in complex backgrounds. Different quantitative results also show that the RESFLCM possesses high signal-to-clutter ratio (SCR) gain and background suppression factor (BSF) with robust detection performance.