  In image fusion tasks, an ideal image decomposition method can bring better
performance. MDLatLRR has done a great job in this aspect, but there is still
exist some space for improvement. Considering that MDLatLRR focuses solely on
the detailed parts (salient features) extracted from input images via latent
low-rank representation (LatLRR), the basic parts (principal features)
extracted by LatLRR are not fully utilized. Therefore, we introduced an
enhanced multi-level decomposition method named dual-decomposed MDLatLRR
(D2-LRR) which effectively analyzes and utilizes all image features extracted
through LatLRR. Specifically, color images are converted into YUV color space
and grayscale images, and the Y-channel and grayscale images are input into the
trained parameters of LatLRR to obtain the detailed parts containing four
rounds of decomposition and the basic parts. Subsequently, the basic parts are
fused using an average strategy, while the detail part is fused using kernel
norm operation. The fused image is ultimately transformed back into an RGB
image, resulting in the final fusion output. We apply D2-LRR to medical image
fusion tasks. The detailed parts are fused employing a nuclear-norm operation,
while the basic parts are fused using an average strategy. Comparative analyses
among existing methods showcase that our proposed approach attains cutting-edge
fusion performance in both objective and subjective assessments.
