Imaging through turbulent media using deep learning method

Abstract

We present deep learning method that can be used to reconstruct high-quality objects through turbulent media mixed with water and milk. The objects are placed behind turbulent media, and a series of speckle patterns are correspondingly recorded. By using many pairs of the recorded speckle patterns and input object images, a designed convolutional neural network (CNN) is fully trained, and then enables the recorded speckle patterns to be processed in real time. The proposed method is promising for imaging through turbulent media, and it is also believed that the proposed method can be applicable in many areas, e.g., imaging and information optics (such as optical encoding).