Latency, high temporal pixel density, and dynamic range are just a few of the benefits of event camera systems over conventional camera systems. Methods and algorithms cannot be applied directly because the output data of event camera systems are segments of synchronization events and experiences rather than precise pixel intensities. As a result, generating intensity photographs from occurrences for other functions is difficult. We use occurrence camera-based contingent deep convolutional connections to establish images and videos from a variable component of the occasion stream of data in this journal article. The system is designed to replicate visuals based on spatio-temporal intensity variations using bundles of spatial coordinates of occurrences as input data. The ability of event camera systems to produce High Dynamic Range (HDR) pictures even in exceptional lighting circumstances, as well as non-blurry pictures in rapid motion, is demonstrated. Furthermore, because event cameras have a transient response of about 1 s, the ability to generate very increased frame rate video content has been evidenced, conceivably up to 1 million arrays per second. The implementation of the proposed algorithms are compared to density images recorded onto a similar gridline in the image of events based on the application of accessible primary data obtained and synthesized datasets generated by the occurrence camera simulation model.
Keywords
Generative Adversarial (GA), High-Dynamic Range (HDR), Stacking-Based Time (SBT), Stacking Based Events (SBE)
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Anderson Stephanie
Anderson Stephanie
Natural and Applied Sciences, Duke Kunshan University, Jiangsu Province, China.
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Cite this article
Anderson Stephanie, “Range Imaging and Video Generation using Generative Adversarial Network", vol.1, no.1, pp. 034-041, January 2021. doi: 10.53759/0088/JBSHA202101005.
