Low-Light Salient Object Detection via Representation Decoupling

In low-light scenes, images often suffer low contrast, poor distinction between the target and the background, and loss of regional information. This makes it challenging for Salient Object Detection (SOD) algorithms to identify and locate the targets. Most existing methods address low-light SOD by first enhancing low-light images and then performing saliency detection. However, splitting these into two sub-tasks may result in negative transfer effects. Additionally, some methods attempt to integrate these two sub-tasks into an end-to-end framework. However, conflicts may arise during the training process due to the differing features required by low-light enhancement and saliency detection. To address this conflict, We propose a decoupled representation network called DRNet, whose core is the construction of a Visual Center Decoupler (VCD). The VCD decouples the learned representations into enhancement-specific and SOD-specific embeddings. This module provides an implicit way to balance the specific requirements of the two subtasks. On the one hand, the enhancement-specific embeddings use RGB illumination constraints and Local Binary Patterns (LBP) feature aggregation to constrain illumination and maintain texture feature stability. On the other hand, the SOD-specific embeddings utilize dynamic multi-scale convolutions to integrate fine-grained details required at multiple scales. Finally, to validate the performance advantages of DRNet, we conduct extensive comparative experiments between the proposed DRNet and existing single-modal methods. Comparative experiments with some representative bi-modal methods further demonstrate the merits of our proposed method.