Abstract
A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability. It is the first time that two-dimensional (2D) images are processed using only one-dimensional1D structuring elements in a morphology-based approach, enabling the real-time hardware implementation of the whole image processing method. A parallel image readout and processing structure is introduced to achieve an ultra-low latency time on the order of nanoseconds, and a hyper-frame resolution in the time domain can be achieved by combining the row-by-row structure and the electrical rolling shutter technique. Experimental results suggest that the expected target can be successfully detected under various interferences with an accuracy of 0.1 pixels (1σ) under the worst sky night test condition and that a centroiding precision of better than 0.03 pixels (1σ) can be reached for static tests. The real-time detection method with high robustness and accuracy is attractive for application to all types of real-time small target detection systems, such as medical imaging, infrared surveillance, and target measurement and tracking, where an ultra-high processing speed is required.
Highlights
Real-time image enhancement and segmentation are critical for weak and small target detection from images with a low signal-to-noise ratio or strong interferences, especially for applications in biomedical image processing[1,2,3], infrared surveillance[4,5,6], and small target measurement and tracking[7,8,9,10]
These algorithms cannot be applied to a real-time system because they have a significant latency time due to the complexity of the algorithm; the latency is further worsened by the separation of image readout and image processing because image processing cannot being until the entire image is read out and restored
We consider two extreme scenarios: (1) when the target is located in the last row of the image, the improvement in the latency time is the traditional image processing time, Tproc; (2) when the target is located in the first row of the image, the target will be identified after the readout of the first row, and the latency time improvement is Tread+Tproc
Summary
Real-time image enhancement and segmentation are critical for weak and small target detection from images with a low signal-to-noise ratio or strong interferences, especially for applications in biomedical image processing[1,2,3], infrared surveillance[4,5,6], and small target measurement and tracking[7,8,9,10]. Several different approaches have been used for detection of such small and dim targets, for example, wavelet-based algorithms, inter-frame differencebased algorithms and filter-based algorithms[13,14,15,16] These algorithms cannot be applied to a real-time system because they have a significant latency time due to the complexity of the algorithm; the latency is further worsened by the separation of image readout and image processing because image processing cannot being until the entire image is read out and restored. The parallel structure-based 1D image processing method with a hyper-frame temporal resolution is attractive for high-speed time-critical applications such as medical imaging, target tracking and measurement and computer-vision assisted robotics[27]. A remote sensing satellite requires an ultra-high processing and update speed for attitude determination doi:10.1038/lsa.2018.6
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