Minimizing Ghosting in High Dynamic Range Images and Videos with Hybrid Approaches and Event Guidance

Date

2025-9-1

Author

Alpay, Kadir Cenk

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The increased interest in consumer-grade high dynamic range (HDR) images and videos in recent years has caused a proliferation of HDR deghosting algorithms. Despite numerous proposals, a fast, memory-efficient, and robust algorithm has been difficult to achieve. In this thesis, we address this problem by first leveraging the power of attention and U-Net-based neural architectures and using a conservative hybrid deghosting strategy to enable the deployment to hardware-constrained devices. Then, we explore the use of event data with the bracketed low dynamic range (LDR) RGB image data to gain temporal precision and HDR information on the dynamics of the scene during the capture of the LDR bracket to guide the minimization of ghosting artifacts further.

Subject Keywords

HDR deghosting, Event cameras, Deep networks

URI

https://hdl.handle.net/11511/116083

Collections

Graduate School of Natural and Applied Sciences, Thesis