Dual domain multi-scale feature enhancement network for light field denoising

Noise significantly degrades light field (LF) imaging quality, particularly in handheld LF cameras due to the light-scattering effects of microlens arrays. However, current LF denoising methods suffer from several limitations: (1) predominant focus on the pixel domain restricts sufficient exploration of LF frequency characteristics; (2) excessive dependence on local information or straightforward addition/concatenation for integrating global and local features impairs global structural consistency and introduces artifacts. Therefore, we propose a dual domain multi-scale network (DDMSN) that integrates frequency-domain noise priors with pixel-domain texture and epipolar geometric structure awareness. Specifically, in the frequency domain module, we propose a dual frequency collaborative enhancement (DFCE) strategy for differentiated treatment of high frequency noise characteristics and valid structural information, and adaptively enhance low frequency and high frequency band components to achieve a fine-grained restoration of the frequency domain structure. In the pixel domain module, to achieve texture detail restoration and structural consistency preservation, we introduce a global-local feature interaction (GLFI) mechanism embedded within the encoder-decoder architecture, which fuses spatial-angular local features with EPI global features through channel attention weighting, optimizing local receptive fields and global structural consistency dependencies jointly. Extensive experiments on synthetic and real-world LF noise datasets demonstrate that our method achieves significant improvements over existing methods in both qualitative visual results and quantitative metrics.