The field of hyperspectral image processing is moving towards the development of more sophisticated and efficient methods for image fusion, super-resolution, and unmixing. Researchers are exploring new techniques to address the ill-posed nature of these problems, such as incorporating ill-posed priors, low-rankness, and smoothness constraints. Additionally, there is a growing interest in using tensor regularization and subspace frameworks to improve computational efficiency and accuracy. Noteworthy papers in this area include: PIF-Net, which proposes a fusion framework that explicitly incorporates ill-posed priors to effectively fuse multispectral and hyperspectral images. Low-rankness and Smoothness Meet Subspace, which introduces a unified tensor regularizer that jointly encodes low-rankness and local smoothness priors under a subspace framework. Towards Globally Predictable k-Space Interpolation, which proposes a white-box Transformer framework for k-space interpolation that better exploits its global structure.