A DT-CWT-based infrared-visible image fusion method for smart city

Following the development of smart city, informative images play a more and more important role in recognition, detection, and perception. As an efficient way, image fusion technique integrates information from multiple images. Multi-scale transform (MST) and sparse representation (SR) are widely used in infrared-visible image fusion. Traditional MST-based fusion methods are difficult to represent all features of source images. At the same time, traditional SR-based fusion methods do not consider morphological information of image features in dictionary learning processes. To overcome the defects of both MST- and SR-based fusion methods, this paper presents a infrared-visible image fusion framework by combining double-tree complex wavelet transform (DT-CWT) and SR. The source images are decomposed and clustered into high and low-pass bands by DT-CWT. The high-pass bands are fused by the sum modified-Laplacian (SML). The low-pass bands are fused by SR-based approach. The fused high- and low-pass bands are integrated and reconstructed by DT-CWT to form the final fused image. Comparing with five mainstream image fusion solutions, the proposed fusion framework can achieve state-of the-art performance in infrared-visible fusion images.