… [27] designed a deep encoderdecoder network by considering the physical formulation of
reflections in digital imaging. Zhang et al. [29] proposed a customized exclusion loss that …

… -based GMM prior to model the natural image for reflection removal. Deep learning based
methods Some recent works start to employ learning based methods in reflection removal …

… prior and the l0 gradient sparsity prior are used in [5] to formulate the optimization problem
for reflection … for modelling patch prior to exploit the ghosting effects on reflection. The above …

… transmission layer recovery, we build our generative prior-based model on one-step diffusion
(… A generic deep architecture for single image reflection removal and image smoothing. In …

… reflections, then we introduce the detailed formulation of content prior based on the region
labels, and finally we introduce our gradient priors for background and reflection, respectively. …

… for deep-learning depth estimation on water scenes via intra-frame priors, known as reflection
… In the first stage, a water segmentation network is performed to separate the reflection …

… Reflection-structure Inconsistency Prior Network (MRIP-Net), which leverages prior knowledge
of reflection … To fuse the encoder and decoder features, we proposed the Deep Structure …

… reflection-contaminated image and the reflection-free image empirically and theoretically. We
include the dark channel prior to restore transmission layers from reflection… , the four deep-…

… for reflection removal if certain prior knowledge [1] is absent. Unlike single image reflection
… Therefore, the potential of deep learning on reflection removal had been unexplored for a …

… at the end of the unit, just prior to the unit’s in-class exam. The … direct effect of reflection on
the use of deep learning strategies (… reflection intervention produced an indirect effect on deep …