Abstract:

Modern warfare highly relies on the carriers such as images to collect intelligence. The images obtained in foggy conditions can interfere with the clear presentation of a battlefield scene and also conceal the important features, thus affecting the acquisition of battlefield information. In view of the common issues, such as color distortion and image detail loss, of current image dehazing algorithms, this paper proposes a multi-scale feature interaction dehazing network (MFI-DehazeNet), which uses a hybrid architecture of convolutional neural network (CNN) and Transformers. The MFI-DehazeNet uses an encoder-decoder structure to achieve a single image dehazing in an end-to-end manner. First, a multi-scale feature interaction module that enables cross-scale fusion of CNN network features is introduced inMFI-DehazeNet. And then the Transformer structure is improved by using a global feature expression module to boost the network’s global expression capability, thus addressing the receptive field limitations of convolutional structures. The output from the encoder, which integrates the two heterogeneous architectures of CNN and Transformer networks, is processed through the feature reconstruction module (i.e., the decoder) to restore and reconstruct dehazed images. Experimental results indicate that MFI-DehazeNet outperforms other algorithms in dehazing both synthetic and real hazy images.

Key words: image dehazing, transformer, convolutional neural network, hybrid architecture