A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Translation

• URL: http://arxiv.org/abs/2410.15068v1
• Date: Sat, 19 Oct 2024 11:11:58 GMT
• Title: A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Translation
• Authors: Hrishav Bakul Barua, Stefanov Kalin, Lemuel Lai En Che, Dhall Abhinav, Wong KokSheik, Krishnasamy Ganesh,
• Abstract summary: Low Dynamic Range (LDR) to High Dynamic Range () image translation is an important computer vision problem. Most current state-of-the-art methods require high-quality paired LDR, datasets for model training. We propose a modified cycle-consistent adversarial architecture and utilize unpaired LDR, datasets for training.
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• Abstract: Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is an important computer vision problem. There is a significant amount of research utilizing both conventional non-learning methods and modern data-driven approaches, focusing on using both single-exposed and multi-exposed LDR for HDR image reconstruction. However, most current state-of-the-art methods require high-quality paired {LDR,HDR} datasets for model training. In addition, there is limited literature on using unpaired datasets for this task where the model learns a mapping between domains, i.e., LDR to HDR. To address limitations of current methods, such as the paired data constraint , as well as unwanted blurring and visual artifacts in the reconstructed HDR, we propose a method that uses a modified cycle-consistent adversarial architecture and utilizes unpaired {LDR,HDR} datasets for training. The method introduces novel generators to address visual artifact removal and an encoder and loss to address semantic consistency, another under-explored topic. The method achieves state-of-the-art results across several benchmark datasets and reconstructs high-quality HDR images.

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