An Attention-Based Network for Single Image HDR Reconstruction

High dynamic range (HDR) imaging can represent a great range of real-world luminosity. In contrast, the traditional low dynamic range (LDR) imaging fails to represent a wide range of luminance since most digital cameras can capture a limited range of light intensity in a natural scene. Recent advances in deep learning allow reconstructing an HDR image from a single LDR image and surpass conventional methods performance. In this work, we propose a novel CNN for HDR image reconstruction based on residual learning and attention mechanism. The proposed network adopts an autoencoder structure with residual blocks trained in a fully end-to-end manner. Residual learning boosts the performance by optimizing the network to converge faster. Moreover, the attention mechanism allows the network to select and enhance meaningful features that will contribute to the reconstruction of the HDR image. In addition, we employ a contextual attention module to perform patch replacement on deep feature maps to help recover information in over-exposed areas. Extensive quantitative and qualitative experiments on public HDR datasets demonstrate the ability of our proposed method to effectively reconstruct a visually pleasing HDR image from a single LDR image and outperform existing approaches.