HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging

Researchers have introduced HDRAgent, a framework for multi-exposure high-dynamic-range imaging that adaptively selects reconstruction strategies based on scene conditions, marking what its creators call the first agent-driven approach to the task [1]. The system, detailed in a paper submitted to arXiv on June 8, departs from conventional feed-forward HDR reconstruction pipelines, which the authors note are susceptible to ghosting artifacts in complex dynamic scenes [1]. HDRAgent instead employs a multimodal large language model, or MLLM, to perceive scene content and guide a set of specialized tools [1]. A fine-grained contextual knowledge matching module retrieves relevant historical cases and tool knowledge, organizing them into structured evidence that the MLLM uses for adaptive tool scheduling [1]. After each reconstruction attempt, a perception–distortion feedback mechanism transforms quality assessments and artifact diagnoses into structured feedback, which is accumulated in memory to refine future strategy selection [1]. For scenes where extreme motion defeats conventional alignment, the framework uses an agent-guided generative alignment strategy that parses dynamic regions and reconstructs unreliable content in non-reference frames under guidance from the reference frame [1]. The paper reports that HDRAgent reduces ghosting and local artifacts while achieving competitive or superior objective performance and visual quality [1]. The work appears within a broader landscape of agentic computer-vision research; a separate study from May 2026 on arXiv explores agent-driven frameworks for other imaging tasks, reflecting growing interest in MLLM-based adaptive pipelines [2]. Earlier arXiv contributions from March 2026 and May 2024 similarly investigate multimodal models for visual reasoning, underscoring the technical lineage on which HDRAgent builds [3][4].