Ground based telescope imaging suffers from interference from the earth’s atmosphere. Fluctuations in the refractive index of the air delay incoming light randomly, resulting in blurred images. A deconvolution from wavefront sensing system is an adaptive optics system that measures the modes in which the light is corrupted (i.e. the wavefront) and corrects it using a process called deconvolution. The wavefront is measured using a wavefront sensor, which consists of an array of microlenses combined with an imaging sensor. Each microlens casts an image of the object unto the imaging sensor, resulting in a collection of images that are differently aberrated depending on their location on the sensor. Conventionally, the wavefront is calculated by measuring the shifts of each microlens image and integrating these shifts over the aperture. This method, however, discards information about the higher order deformations of the microlens images.

In this thesis, a novel method of wavefront reconstruction has been developed which makes use of artificial neural networks in order to extract this higher order information. In order to do this, the images produced by the microlenses are normalized, which is done using a modified version of the blind deconvolution algorithm called TIP. After the normalization, the microlens images are reduced to what they would look like if a point source was observed, instead of the object. With the influence of the object removed, an artificial neural network is used for the estimation of the wavefront.

By using this method, the wavefront can be reconstructed with twice the turbulence strength compared to what is possible with conventional methods. Combining this method with an image deconvolution step results in a real-time image correction system that works up to 10Hz on the tested system, consisting of a desktop PC with an Intel Xeon E5-2630 DUAL CPU and a NVIDIA GeForce GTX 970 GPU.