Vision Restoration for Challenging Army Using Gan and Diffusion Models

Image restoration (IR) is crucial for improving the quality of degraded images, and recent advancements in the diffusion model have prompted exploration into its potential for enhancing this process. Integrating diffusion models into IR tasks has shown promise, outperforming previous GAN- based methods. However, a comprehensive overview of diffusion model-based techniques in IR is lacking. This paper addresses this gap in the field of army by reviewing recent methods leveraging diffusion models for IR, covering learning paradigms, framework designs, and evaluation methodologies. We categorize innovative designs for both conventional and real-world scenarios, aiming to inspire future advancements. Additionally, we provide an overview of datasets, implementation details, and conduct objective comparisons across key tasks. Drawing from existing limitations, we propose challenging directions for future research, including sampling efficiency and model compression.