Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.
Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems.
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.
IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and information across steps of the cascade is transferred using an LSTM.
Reflections often obstruct the desired scene when taking photos through glass panels.
We present a novel formulation to removing reflection from polarized images in the wild.
To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.
Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image.