Joint image fusion and super-resolution for enhanced visualization via semi-coupled discriminative dictionary learning and advantage embedding

In recent years, image fusion has attracted more and more attention, and many excellent methods have emerged. However, only a few studies on joint image fusion and super-resolution have been carried out, and the performance of existing methods is far from that of simple image fusion. To tackle such problem, we propose a novel joint fusion and super-resolution framework based on discriminative dictionary learning. Specifically, we first jointly learn two pairs of low-rank and sparse dictionaries (LRSD) and a conversion dictionary. One pair is used to represent the low-rank and sparse components of low-resolution input images, and the other is used to reconstruct high-resolution fused result; the conversion dictionary is used to establish the relationship between coding coefficients of low-resolution image and high-resolution image. To compensate for the loss of details, structure information compensation dictionary (SICD) is also learned, and the lost information is compensated by SICD and thus visualization of final results is enhanced. To integrate advantages of excellent image fusion methods into the fused and reconstructed results, we propose a deconvolution-based advantage embedding scheme. The experimental results verify the effectiveness and advantages of our method over other competitive ones.