Method of reflection removal based on a generative adversarial network used for training of an adas camera of a vehicle

Abstract

A method of reflection removal based on a GAN used for training of an ADAS vehicle camera includes acquisition, training and inference operations. In a first training operation, data processing hardware acquires two randomly sampled pairs of images from a first image dataset and carries out two simultaneous altering and overlapping of the image pair generating first and second mixed images. In a second training operation, the first image together with a third mixed image are altered, the third mixed image proceeding from a second dataset. The output of the first and second training operations enter the third training operation which is carried out by using the GAN. A third training operation generates first, second and third predicted transmission images; and a machine learning model is optimized for the predicted transmission images as close as possible, compressed and sent to a GAN machine learning block.