AdaptiveNet: a learning-based method for the restoration of optically degraded images

Abstract It is widely recognized that optically captured images can be degraded under non-ideal imaging conditions, such as blurring, defocusing, and the presence of scattering media. In this study, we introduce a novel architecture that integrates an adaptive encoder to enhance the general restoration of optical images. We refer to this architecture as AdaptiveNet, which is capable of adaptively identifying different types of non-ideal imaging conditions and learning to restore images accordingly. Our extensive experimental results indicate that AdaptiveNet outperforms existing methods across a range of optical image restoration tasks.