HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
Researchers have introduced HDRAgent, a novel framework for High Dynamic Range (HDR) imaging that utilizes an agent-driven approach to adaptively select reconstruction strategies. This method aims to mitigate ghosting artifacts common in dynamic scenes by employing a fine-grained contextual knowledge matching module. This module leverages multimodal large language models (MLLMs) to perceive scene conditions, retrieve relevant historical cases and tool knowledge, and schedule adaptive tools. Additionally, a perception-distortion feedback mechanism refines strategies over time, and an agent-guided generative alignment strategy reconstructs unreliable content. AI
IMPACT Introduces an agent-based approach for image reconstruction, potentially improving performance in dynamic visual scenes.