Multi-Attention Learning and Exposure Guidance Toward Ghost-Free High Dynamic Range Light Field Imaging

Due to sensor limitations, the light field (LF) images captured by the LF camera suffer from low dynamic range and are prone to poor exposure. To solve this problem, combining multi-exposure technology with LF camera imaging can achieve high dynamic range (HDR) LF imaging. However, for dynamic scenes, this approach tends to produce disturbing ghosting artifacts and destroy the parallax structure of the generated results. To this end, this paper proposes a novel ghost-free HDR LF imaging method using multi-attention learning and exposure guidance. Specifically, the proposed method first designs a multi-scale cross-attention module to achieve efficient multi-exposure LF feature alignment. After that, a dual self-attention-driven Transformer block is constructed to excavate the geometric information of LF and fuse the aligned LF features. In particular, exposure masks derived from middle-exposure are introduced in the feature fusion to guide the network to focus on information recovery in low- and high-brightness regions. Besides, a local compensation module is integrated to cope with local alignment errors and refine details. Finally, a multi-objective reconstruction strategy combined with exposure masks is employed to restore high-quality HDR LF images. Extensive experimental results on the benchmark dataset show that the proposed method generates HDR LF results with high spatial-angular quality consistency and outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Furthermore, the proposed method can enhance the performance of existing LF applications, such as depth estimation.