Enhanced light field depth estimation through occlusion refinement and feature fusion

Abstract

Light field depth estimation is crucial for various applications, but current algorithms often falter when dealing with complex textures and edges. To address this, we propose a light field depth estimation network based on multi-scale fusion and channel attention (LFMCNet). It incorporates a convolutional multi-scale fusion module to enhance feature extraction and utilizes a channel attention mechanism to refine depth map accuracy. Additionally, LFMCNet integrates the Transformer Feature Fusion Module (TFFM) and Channel Attention-Based Perspective Fusion (CAPF) module for improved occlusion refinement, effectively handling challenges in occluded regions. Testing on the 4D HCI and real-world datasets demonstrates that LFMCNet significantly reduces the Bad Pixel (BP) rate and Mean Square Error (MSE).