CFDM: Cross-Fusion Dehazing Model for Visible Images

Image dehazing helps visual systems adapt to changing weather conditions. Hazy environments significantly reduce image clarity and contrast, which can impair high-level vision tasks such as object detection. To address this issue, this paper proposes a lightweight model for visible image dehazing called the Cross-Fusion Dehazing Model (CFDM). The model restores image clarity through cross-fusion and residual connections. A dynamic dehazing module (DDM) is integrated into CFDM to remove haze while preserving image features. DDM consists of lightweight depthwise separable convolution (DSConv), a dynamic Tanh layer (DyT), and the GeLU activation function. This design improves efficiency while enhancing adaptability to various hazy scenes. In addition, this paper adopts an adaptive fusion strategy to integrate features from different layers. This ensures more effective feature fusion during transmission and facilitates subsequent modules in processing multi-level information. Experimental results show that the proposed model performs well in improving image clarity, preserving details, and removing haze. In addition, the model has a simple structure, low parameter count, and fast inference speed, making it practical and efficient.