VSANet: View-aware Sparse Attention Network for Light Field Image Denoising
This work addresses the challenging problem of light field denoising for computer vision applications, offering a more effective and efficient solution than existing methods.
VSANet introduces a view-aware sparse attention mechanism for light field image denoising, achieving state-of-the-art performance by efficiently exploiting cross-view correlations with linear complexity.
Light field (LF) image denoising is challenging due to the high-dimensional structure of LF data. While noise is independent across sub-aperture images, scene content exhibits strong cross-view correlations. We introduce VSANet, a view-aware sparse attention network for LF denoising. Specifically, we propose a view-aware sparse attention (VSA) block that represents the 4D LF feature map as a unified spatial-angular token space and performs cross-view aggregation via locality-sensitive hashing-based sparse attention. This enables global feature interactions with linear complexity, effectively exploiting LF correlations across views and spatial locations. In addition, we design a feature refinement (FR) block to emphasize informative features in spatial, angular, and epipolar subspaces. The VSA and FR blocks are integrated within a sequential attention refinement module, forming the core of VSANet. Experiments demonstrate VSANet outperforms stateof-the-art LF denoising methods.