RFG-HDR: Representative Feature-Guided Transformer For Multi-Exposure High Dynamic Range Imaging

Multi-exposure fusion is a high dynamic range (HDR) imaging technique that combines multiple low dynamic range (LDR) images of a scene with varying exposure times to produce a single high-quality HDR image. Since each LDR frame is captured with a different exposure time (bias), it is crucial to extract meaningful features from each differently-exposed LDR frame for producing high-quality HDR images. This paper introduces a new contrastive learning method that provides a versatile way of extracting characteristic features from LDR frames by considering the relationship between LDR frames. Additionally, we introduce Representative Feature-Guided Transformer (RFGHDR), a new architecture that utilizes contrastive-learned representations to improve frame alignment and merging. Based on extensive experiments on various datasets, we have found that the RFG-HDR performs better than existing multi-exposure HDR imaging methods in terms of various evaluation metrics. Our work will be released on https://github.com/KeuntekLee/RFG-HDR.