Learning Implicit and Detail-Enhanced Network for Light Field Image Spatial-Angular Super-Resolution

Light field (LF) imaging holds immense promise for applications such as post-capture refocusing and virtual reality. However, its inherent spatial-angular trade-off significantly limits both spatial and angular resolution, restricting its practicality in real-world scenarios. To address these limitations, spatial-angular super-resolution methods have been proposed to simultaneously enhance both dimensions. Yet, existing methods struggle to fully exploit the intertwined spatial-angular correlations and fail to effectively handle sparsely sampled LFs with low spatial resolution, often leading to cumulative errors during reconstruction. In this paper, we propose an Implicit and Detail-Enhanced Network (IDNet) to overcome these challenges. Our IDNet employs 3D convolution for the joint extraction of spatial and angular information, leveraging their interdependencies for more effective LF reconstruction. Additionally, we introduce an implicit detail restoration module that enhances features while encoding positional information to refine fine details. To overcome the limitations of sparse spatial and angular information on high-detail reconstruction and angular consistency in low-resolution LFs, we design a multi-representation enhancement block. This block enhances features by learning pixel differences across multiple directions in diverse representations, effectively capturing intricate details and complex correlations. Thanks to these designs, our IDNet reconstructs novel views with finer details, effectively learns occlusion relationships, and ensures geometric consistency. Experimental results on benchmark datasets demonstrate its superior quantitative and qualitative performance. The code is publicly available at https://github.com/ldyorchid/IDNet.