Multiview Ghost-Free Image Enhancement for In-the-Wild Images with Unknown Exposure and Geometry

Abstract

The multiview low dynamic range images captured with sparse camera arrangement under ill-lighting conditions contain highlighted and shadow regions due to over-exposed and under-exposed regions. The processing of these images produces contrast distortion, and it is challenging to maintain relative brightness with color consistency. Moreover, the disparity map estimation faces the challenges of holes and artifacts due to a wide baseline and poor visibility, with a shared view of vision. In this article, we propose a multiview ghost-free image enhancement strategy for in-the-wild images with unknown exposure and geometry. We address the complex geometric alignment problem for a wide variational baseline among multiple sparsely arranged cameras. The features among multiple viewpoints are detected and matched for the image restoration. The restored image contains highlighted and shadow regions with a color imbalance problem. We synthesize virtual images following the intensity mapping function, which compensates for the relative brightness and color distortions. Finally, we fuse all the images to obtain high-quality images. The proposed method is more frequent and feasible for future multiview systems with varying baselines without relying on disparity maps. The experimental results demonstrate that the proposed method outperformed the state-of-the-art approaches.