Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
DEEP LEARNING-BASED UNROLLED RECONSTRUCTION METHODS FOR COMPUTATIONAL IMAGING
Download
Thesis_CanDenizBezek.pdf
Date
2021-9-08
Author
Bezek, Can Deniz
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
644
views
591
downloads
Cite This
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.
Subject Keywords
computational imaging
,
spectral imaging
,
inverse problems
,
image reconstruction
,
deep learning
,
algorithm unrolling
,
truncated convolution
URI
https://hdl.handle.net/11511/93159
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Anlık Spektral Görüntüleme için Tasarım Eniyileme
Ayazgök, Suleyman; Öktem, Sevinç Figen (2019-08-22)
Snapshot spectral imaging enables to reconstructspectral images from a multiplexed single-shot measurement.Since an inversion is required to form the spectral images com-putationally, quantitative characterization of their performanceis essential to optimize the design. In this paper, we analyze theoptimal design of a snapshot spectral imaging technique. Thissnapshot multi-spectral imaging technique uses a diffractive lenscalled generalized photon sieve, and vari...
Efficient algorithms for convolutional inverse problems in multidimensional imaging
Doğan, Didem; Öktem, Figen S.; Department of Electrical and Electronics Engineering (2020)
Computational imaging is the process of indirectly forming images from measurements using image reconstruction algorithms that solve inverse problems. In many inverse problems in multidimensional imaging such as spectral and depth imaging, the measurements are in the form of superimposed convolutions related to the unknown image. In this thesis, we first provide a general formulation for these problems named as convolutional inverse problems, and then develop fast and efficient image reconstruction algorith...
Compressive spectral imaging using diffractive lenses and multi-spectral sensors with learned reconstruction and joint optimization
Gündoğan, Utku; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2022-2)
Compressive spectral imaging aims to reconstruct the entire three-dimensional spectral cube from a few measurements, ideally with a snapshot capability. Recently various spectral imaging modalities have been developed by exploiting diffractive lenses. Another line of development in this area is enabled by spectral filter arrays which resulted in multi-spectral sensors. In this thesis, we first review an existing compressive spectral imaging modality with diffractive lenses and analyze its performance using ...
End-to-end learned image compression with conditional latent space modelling for entropy coding
Yeşilyurt, Aziz Berkay; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2019)
This thesis presents a lossy image compression system based on an end-to-end trainable neural network. Traditional compression algorithms use linear transformation, quantization and entropy coding steps that are designed based on simple models of the data and are aimed to be low complexity. In neural network based image compression methods, the processing steps, such as transformation and entropy coding, are performed using neural networks. The use of neural networks enables transforms or probability models...
Automatic building extraction from high resolution satellite images for map updating: A model based approach
San, D. Koc; TÜRKER, MUSTAFA (2007-10-12)
An approach was developed for automatically updating the buildings of an existing vector database from high resolution satellite images using spectral image classification, Digital Elevation Models (DEM) and the model-based extraction techniques. First, the areas that contain buildings are detected using spectral image classification and the normalized Digital Surface Model (nDSM). The classified output provides the shapes and the approximate locations of the buildings. However, those buildings that have si...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
C. D. Bezek, “DEEP LEARNING-BASED UNROLLED RECONSTRUCTION METHODS FOR COMPUTATIONAL IMAGING,” M.S. - Master of Science, Middle East Technical University, 2021.