Unlike traditional optical cameras, Event Cameras are a new type of neuromorphic vision sensors which generate asynchronous streams of events in response to changes in log-illumination at each pixel. These devices are, therefore, extremely fast, allow for imaging while the device is moving, and enable low-power space imaging equally well during daytime as well as night. It can compensate for the limitations of optical equipment detection and meet the current space object detection requirements. Based on the background of space object detection and the optical observation technology of event cameras, the research status and several future development trends of event-based space object detection methods are summarized and reviewed. Firstly, the basic principle of event cameras and the advantages and disadvantages of their application in the aerospace field are described. Then the technical development of event cameras in space object detection, recognition and tracking are introduced. Finally, the development direction of event cameras in space object detection is discussed.
For high dynamic range image reconstruction in dynamic scenes, this paper proposes a multi-exposure sequence image fusion method based on histogram matching and convolutional neural network. First, image alignment is performed on the reference image based on the histogram matching method. Then, a multi-exposure fusion model based on convolutional neural network is constructed for the aligned multi-exposure sequence images. Finally, the fusion model is trained based on the training data, and the multi-exposure sequence image fusion in dynamic scenes is realized. The experimental results show that the method proposed in this chapter effectively avoids the influence of dynamic scenes on the fusion of multi-exposure sequence images, and at the same time can generate high-quality fused images.
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