1. A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Translation
- Author
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Barua, Hrishav Bakul, Kalin, Stefanov, Che, Lemuel Lai En, Abhinav, Dhall, KokSheik, Wong, and Ganesh, Krishnasamy
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Graphics ,Computer Science - Machine Learning ,Computer Science - Robotics ,Artificial intelligence, Computer vision, Machine learning, Deep learning ,I.3.3 ,I.4.5 - 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., Comment: Submitted to IEEE
- Published
- 2024