Deep-learning method for separating reflection and transmission images visible at a semi-reflective surface in a computer image of a real-world scene

A non-transitory computer readable storing code executable by a processor to perform a method comprising:

• synthesizing training data for a deep learning network by applying a plurality of manipulations to a training reflection image and a training transmission image for a semi-reflective surface representation, the plurality of manipulations simulating behaviors observed in real-world data;
• training the deep learning network to learn a residual representation of a reflection and transmission relative to input images, using the training data;
• receiving, as input to the deep learning network, polarization images of a real-world scene having a semi-reflective surface;
• outputting, by the deep learning network, the residual representation of the reflection and transmission for the semi-reflective surface of the real-world scene.

A system, comprising:

• a memory;
• and
• at least one processor for:
• synthesizing training data for a deep learning network by applying a plurality of manipulations to a training reflection image and a training transmission image for a semi-reflective surface representation, the plurality of manipulations simulating behaviors observed in real-world data;
• training the deep learning network to learn a residual representation of a reflection and transmission relative to input images, using the training data;
• receiving, as input to the deep learning network, polarization images of a real-world scene having a semi-reflective surface;
• outputting, by the deep learning network, the residual representation of the reflection and transmission for the semi-reflective surface of the real-world scene.