Restoration of Laser Interference Image Based on Large Scale Deep Learning

Abstract

Although the deep learning method has achieved some results in the challenging task of repairing the missing areas of images over the years, there is no report on the images disturbed by the laser entering the field of view. We propose a restoration model that can restore the laser interference image, the corresponding countermeasure network deep learning model and a new model training method, and no additional manual marking work is required in the data training set. After a large number of experiments, the loss function can rapidly converge under this method, accurately give the reasonable reconstruction results of the image disturbed by laser, and significantly improve the scores of many common image quality evaluation methods: high-quality repair results are obtained on the laser interference composite dataset of face (CelebA), Stanford automobile, aircraft, buildings (Facade) and satellite images. It has the characteristics of fast training speed, strong robustness, modular model design and wide application.