Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning

Medical image fusion is important in image-guided medical diagnostics, treatment, and other computer vision tasks. However, most current approaches assume that the source images are noise-free, which is not usually the case in practice. The performance of traditional fusion methods decreases significantly when images are corrupted with noise. It is therefore necessary to develop a fusion method that accurately preserves detailed information even when images are corrupted. However, suppressing noise and enhancing textural details are difficult to achieve simultaneously. In this paper, we develop a novel medical image fusion, denoising, and enhancement method based on low-rank sparse component decomposition and dictionary learning. Specifically, to improve the discriminative ability of the learned dictionaries, we incorporate low-rank and sparse regularization terms into the dictionary learning model. Furthermore, in the image decomposition model, we impose a weighted nuclear norm and sparse constraint on the sparse component to remove noise and preserve textural details. Finally, the fused result is constructed by combining the fused low-rank and sparse components of the source images. Experimental results demonstrate that the proposed method consistently outperforms existing state-of-the-art methods in terms of both visual and quantitative evaluations.