High dynamic range imaging via gradient-aware context aggregation network


Obtaining a high dynamic range (HDR) image from multiple low dynamic range images with different exposures is an important step in various computer vision tasks. One of the ongoing challenges in the field is to generate HDR images without ghosting artifacts. Motivated by an observation that such artifacts are particularly noticeable in the gradient domain, in this paper, we propose an HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain. The proposed method generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain. The context information in a large area helps to reconstruct the contents contaminated by saturation and misalignments. Specifically, an additional gradient stream and the supervision in the gradient domain are applied to incorporate the gradient information in HDR imaging. To use the context information captured from a large area while preserving spatial resolution, we adopt dilated convolutions to extract multi-scale features with rich context information. Moreover, we build a new dataset containing 40 groups of real-world images from diverse scenes with ground truth to validate the proposed model. The samples in the proposed dataset include more challenging moving objects inducing misalignments. Extensive experimental results demonstrate that our proposed model outperforms previous methods on different datasets in terms of both quantitative measure and visual perception quality.

In Pattern Recognition