Hesaplamalı görüntüleme için derin öğrenme tabanlı geriçatım yöntemleri

Computational imaging is the process of forming images from indirect measurements using computation. In this thesis, we develop deep learning-based unrolled reconstruction methods for various computational imaging modalities. Firstly, we develop two deep learning-based reconstruction methods for diffractive multi-spectral imaging. The first approach is based on plug-and-play regularization with deep denoisers whereas the second one is an end-to-end learned reconstruction based on unrolling. Secondly, we consider general multidimensional imaging systems whose measurements involve convolution and superposition. We formulate a realistic truncated image formation model and develop a deep learning-based unrolled reconstruction method to solve the associated inverse problem. The performance is illustrated for single-frame deconvolution and diffractive multi-spectral imaging applications. Thirdly, we extend the deep learning-based unrolled reconstruction methods to a compressive spectral imaging modality by considering both the conventional and truncated image formation models. The performance of all the developed methods is comparatively evaluated for sample applications using different convolutional neural network (CNN) architectures, including U-Net. Results illustrate the superior performance of the developed unrolled learned reconstruction methods over purely analytical methods for all simulation settings. We also demonstrate the generalization capability of the developed unrolled reconstruction methods through extensive simulations and perform model mismatch analyses to illustrate the improvement gained with the truncated image formation model.