Collaborative multi-stage attention with integrated cues for light field denoising

Light field (LF) denoising is pivotal for enhancing downstream LF applications. However, most existing methods focus primarily on removing noise signals, lacking sufficient preservation and utilization of the intrinsic structural consistency of LF, which leads to suboptimal denoising performance and compromised geometric relationships. To this end, we propose a collaborative multi-stage attention mechanism that integrates complementary cues from residuals, geometric constraints, and global information. Specifically, first, we design a residual-driven channel attention module to leverage residual analysis for coarse denoising; second, an epipolar-guided cross attention module to explicitly preserve structural consistency across views by leveraging epipolar constraints; and finally, a global domain refinement module that performs deep optimization of texture and structure via multi-head self-attention. Experiments on synthetic and real-world LFs demonstrate that the proposed method maintains the consistency of LF structure and simultaneously outperforms state-of-the-art methods in denoising performance.