The field of image super-resolution and reconstruction is rapidly advancing, with a focus on developing innovative methods to improve image quality and fidelity. Recent research has explored the use of deep learning techniques, such as diffusion models and latent diffusion models, to enhance image super-resolution and reconstruction. Additionally, there is a growing interest in developing methods that can handle complex scenes and preserve image details, particularly in remote sensing and medical imaging applications. Noteworthy papers in this area include Decoupling Multi-Contrast Super-Resolution, which proposes a novel framework for multi-contrast super-resolution, and Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution, which introduces a semantic-guided diffusion framework for single-step image super-resolution. Other notable works include High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution and JSover: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections, which propose innovative methods for accelerating image super-resolution and improving multi-material decomposition.