Image Features Extraction and Fusion Based on Joint Sparse Representation

In this paper, a novel joint sparse representation-based image fusion method is proposed. Since the sensors observe related phenomena, the source images are expected to possess common and innovation features. We use sparse coefficients as image features. The source image is represented with the common and innovation sparse coefficients by joint sparse representation. The sparse coefficients are consequently weighted by the mean absolute values of the innovation coefficients. Furthermore, since sparse representation has been significantly successful in the development of image denoising algorithms, our method can carry out image denoising and fusion simultaneously, while the images are corrupted by additive noise. Experiment results show that the performance of the proposed method is better than that of other methods in terms of several metrics, as well as in the visual quality.