Dual-Stream Adaptive Convergent Low-Light Image Enhancement Network Based on Frequency Perception

Low-light image enhancement is a crucial area of research in computer vision, aimed at recovering normally exposed images from low-light images to facilitate high-level vision tasks such as target detection, tracking, and recognition. However, the convolutional neural networks commonly used in this field have a bias towards extracting low-frequency local structural features in the spatial domain, resulting in unclear texture details in the enhanced images. To address this limitation, this paper proposes a novel frequency-domain perception-based model, called DSFPNet. This model has three unique features. First, the recursive The Hadamard product method is used to model long-range relationships of the Transformer in the feature extraction. Second, a bilateral gating mechanism is added to the feedforward network to filter out useless information and improve the nonlinear modeling capability of the module. Third, cross-layer connectivity of low-frequency features is added to maintain the structural stability of features at different levels. In addition, the proposed model uses an EFF (Enhancing Frequency Features) module to extract frequency features and selectively fuse the three high frequencies. The fused features are then aligned and reconstructed with the low-frequency features to obtain the final frequency features, which helps the model recover enhanced image texture details. Through a large number of experimental results on the two public datasets, the proposed DSFPNet model shows excellent performances and outperforms many existing state-of-the-art methods. The model exhibits good potential and is expected to advance the field of low-light image enhancement in computer vision.