Abstract 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.

中文翻译：

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联合图像融合和超分辨率通过半耦合判别字典学习和优势嵌入增强可视化

摘要 近年来，图像融合受到越来越多的关注，涌现出许多优秀的方法。然而，联合图像融合和超分辨率方面的研究还很少，现有方法的性能与简单的图像融合相去甚远。为了解决这个问题，我们提出了一种基于判别字典学习的新型联合融合和超分辨率框架。具体来说，我们首先联合学习两对低秩稀疏字典（LRSD）和一个转换字典。一对用于表示低分辨率输入图像的低秩和稀疏分量，另一对用于重建高分辨率融合结果；转换字典用于建立低分辨率图像和高分辨率图像的编码系数之间的关系。为了补偿细节的丢失，还学习了结构信息补偿字典（SICD），并通过 SICD 补偿丢失的信息，从而增强了最终结果的可视化。为了将优秀图像融合方法的优势整合到融合和重建的结果中，我们提出了一种基于反卷积的优势嵌入方案。实验结果验证了我们的方法相对于其他竞争方法的有效性和优势。