High Dynamic Range Deghosting

High dynamic range (HDR) deghosting aims to create clear, ghost-free HDR images from multiple low dynamic range (LDR) images, a challenging task due to motion blur and saturation in the input LDRs. Recent research focuses on leveraging deep neural networks, particularly diffusion models and recurrent networks like LSTMs, often incorporating techniques like attention mechanisms and feature alignment to improve ghost removal and detail preservation. These advancements are improving the quality and efficiency of HDR image generation, with applications in photography, virtual reality, and other fields requiring high-fidelity image reconstruction from multiple exposures. Furthermore, research is exploring semi-supervised and few-shot learning approaches to reduce the reliance on large, labeled datasets.