Saliency Detection Based on Context-aware Cross-layer Feature Fusion for Light Field Images

Saliency detection of light field images is a key technique in applications such as visual tracking, target detection, and image compression. However, the existing deep learning methods ignore feature differences and global contextual information when processing features, resulting in blurred saliency maps and even incomplete detection objects and difficult background suppression in scenes with similar foreground and background colors, textures, or background clutter. A context-aware cross-layer feature fusion-based saliency detection network for light field images is proposed. First, a cross-layer feature fusion module is built to select adaptively complementary components from input features to reduce feature differences and avoid inaccurate integration of features in order to more effectively fuse adjacent layer features and informative coefficients; Meanwhile, a Parallel Cascaded Feedback Decoder (PCFD) is constructed using the cross-layer feature fusion module to iteratively refine features using a multi-level feedback mechanism to avoid feature loss and dilution of high-level contextual features; Finally, a Global Context Module (GCM) generates multi-scale features to exploit the rich global context information in order to obtain the correlation between different salient regions and mitigate the dilution of high-level features. Experimental results on the latest light field dataset show that the textual method outperforms the compared methods both quantitatively and qualitatively, and is able to detect accurately complete salient objects and obtain clear saliency maps from similar front/background scenes.