High dynamic range (HDR) tone mapping techniques have been widely studied to effectively represent the broad dynamic range of real-world scenes. However, generating an HDR image from multiple low dynamic range (LDR) images captured at different exposure levels can introduce ghosting artifacts in dynamic scenes. Moreover, methods that estimate HDR information from a single LDR image often suffer from inherent accuracy limitations. To overcome these limitations, this study proposes a novel image processing technique that extends the dynamic range of a single LDR image. This technique achieves the goal through leveraging a Convolutional Neural Network (CNN) to generate a synthetic Near-Infrared (NIR) image—one that emulates the characteristic of real NIR imagery being less susceptible to diffraction, thus preserving sharper outlines and clearer details. This synthetic NIR image is then fused with the original LDR image, which contains color information, to create a tone-distributed HDR-like image. The synthetic NIR image is produced using a lightweight U-Net-based autoencoder, where the encoder extracts features from the LDR image, and the decoder synthesizes a synthetic NIR image that replicates the characteristics of a real NIR image. To enhance feature fusion, a cardinality structure inspired by Extended-Efficient Layer Aggregation Networks (E-ELAN) in You Only Look Once Version 7 (YOLOv7) and a modified convolutional block attention module (CBAM) incorporating a difference map are applied. The loss function integrates a discriminator to enforce adversarial loss, while VGG, structural similarity index, and mean squared error losses contribute to overall image fidelity. Additionally, non-reference image quality assessment losses based on BRISQUE and NIQE are incorporated to further refine image quality. Experimental results demonstrate that the proposed method outperforms conventional HDR techniques in both qualitative and quantitative evaluations.
Lee et al. (Sun,) studied this question.