Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.
Mekides Assefa Abebe, "Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction" in Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning, 2021, pp 43 - 48, https://doi.org/10.2352/issn.2694-118X.2021.LIM-43