A Medical Image Fusion Method based on MDLatLRRv2

Since MDLatLRR only considers detailed parts (salient features) of input images extracted by latent low-rank representation (LatLRR), it doesn't use base parts (principal features) extracted by LatLRR effectively. Therefore, we proposed an improved multi-level decomposition method called MDLatLRRv2 which effectively analyzes and utilizes all the image features obtained by LatLRR. Then we apply MDLatLRRv2 to medical image fusion. The base parts are fused by average strategy and the detail parts are fused by nuclear-norm operation. The comparison with the existing methods demonstrates that the proposed method can achieve state-of-the-art fusion performance in objective and subjective assessment.

Res2NetFuse: A Fusion Method for Infrared and Visible Images

This paper presents a novel Res2Net-based fusion framework for infrared and visible images. The proposed fusion model has three parts: an encoder, a fusion layer and a decoder, respectively. The Res2Net-based encoder is used to extract multi-scale features of source images, the paper introducing a new training strategy for training a Res2Net-based encoder that uses only a single image. Then, a new fusion strategy is developed based on the attention model. Finally, the fused image is reconstructed by the decoder. The proposed approach is also analyzed in detail. Experiments show that our method achieves state-of-the-art fusion performance in objective and subjective assessment by comparing with the existing methods.