The recent advancements in the field of imaging and image restoration have shown a significant shift towards integrating physical models with deep learning techniques. This integration aims to address the inherent challenges in various imaging scenarios, such as dynamic scene capture, atmospheric disturbances, and sensor limitations. Notably, there is a growing emphasis on developing methods that not only enhance image quality but also provide accurate uncertainty quantification, which is crucial for reliable downstream applications. The use of variational Bayesian frameworks and deep priors in image restoration tasks, such as dehazing and satellite image restoration, has demonstrated superior performance by incorporating uncertainties and physical constraints into the models. Additionally, the field is witnessing innovative approaches to hyperspectral imaging, where advancements in hardware and computational methods are enabling faster and more accurate capture of spectral and depth information, even in dynamic environments. These developments are paving the way for more robust and versatile imaging systems that can operate under diverse and challenging conditions.

Noteworthy papers include one that introduces a variational Bayesian framework for single image dehazing, effectively addressing the uncertainties in haze degradation, and another that presents a novel method for hyperspectral 3D imaging in dynamic scenes, significantly reducing acquisition time and cost while maintaining high accuracy.