LarTap: A Luminance-Aware Framework With Text-Correlation Priors for Multi-Exposure Image Fusion
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Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods.Description
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40 Engineering, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, 4605 Data Management and Data Science, 4607 Graphics, Augmented Reality and Games, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, Artificial Intelligence & Image Processing, 4006 Communications engineering, 4009 Electronics, sensors and digital hardware
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IEEE Transactions on Circuits and Systems for Video Technology, ISSN: 1051-8215 (Print); 1558-2205 (Online), Institute of Electrical and Electronics Engineers (IEEE), PP(99), 1-1. doi: 10.1109/tcsvt.2025.3562564
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This article has been accepted for publication in IEEE Transactions on Circuits and Systems for Video Technology. This is the author's version which has not been fully edited and content may change prior to final publication.