The field of image enhancement and restoration is witnessing significant advancements with the development of novel deep learning models and techniques. Recent research has focused on improving the accuracy and robustness of image restoration methods, particularly in challenging environments such as nighttime or underwater conditions. Notably, the integration of physical models and constraints into deep learning architectures has shown promising results, enabling more accurate and realistic image enhancements. Furthermore, the use of attention mechanisms, diffusion models, and capsule clustering has improved the performance of image restoration methods. Overall, the field is moving towards more sophisticated and specialized approaches, leveraging the strengths of both traditional image processing techniques and deep learning methods.

Noteworthy papers include: Cloud Optical Thickness Retrievals Using Angle Invariant Attention Based Deep Learning Models, which proposes a novel angle-invariant attention-based deep model for cloud optical thickness estimation. Astrophotography turbulence mitigation via generative models, which introduces a generative restoration method for mitigating atmospheric turbulence in astronomical images. Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement, which presents a novel dual-stream architecture for underwater image enhancement that integrates physical models with capsule clustering-based feature representation learning.