Deep learning methods for image reconstruction from angularly sparse data for CT and SAR imaging

Sean Thammakhoune; Emre Yavuz

doi:10.1117/12.2558953

2 June 2020 Deep learning methods for image reconstruction from angularly sparse data for CT and SAR imaging

Sean Thammakhoune, Emre Yavuz

Proceedings Volume 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII; 1139306 (2020) https://doi.org/10.1117/12.2558953
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

In this paper we apply deep learning methods to improve image reconstruction from angularly sparse data in Computed Tomography (CT) and SAR imaging. In CT, image reconstruction from sparse views is desirable to reduce X-ray exposure for patients, improving reconstruction time. It is also desirable to reduce the number of pulses used to reconstruct far-field objects in SAR imaging. Conventional algorithms must often incorporate a priori knowledge while successful approaches such as total variation regularization (TV) are limited to signal-tonoise ratio ranges cannot match the inconsistencies of practical application.¹ Instead, we propose to formulate the image reconstruction problem as an optimization problem. In this approach, a recurrent neural network (RNN) is used to unfold a given, fixed number of iterations of an iterative solver. We verify the performance of our method using numerical data and compare it with more traditional approaches.

Citation Download Citation

Sean Thammakhoune and Emre Yavuz "Deep learning methods for image reconstruction from angularly sparse data for CT and SAR imaging", Proc. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 1139306 (2 June 2020); https://doi.org/10.1117/12.2558953

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