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Injecting Image Priors into Learnable Compressive Subsampling

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages1735-1739
Number of pages5
ISBN (Electronic)978-9-0827-9701-5
ISBN (Print)978-1-5386-3736-4
DOIs
Publication statusPublished - Sep 2018
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

Many medical (computerized tomography, magnetic resonance imaging) and astronomy imaging problems (Square Kilometre Array), spectroscopy and Fourier optics attempt at reconstructing high quality images in the pixel domain from a limited number of samples in the frequency domain. In this paper, we extend the problem formulation of learnable compressive subsampling [1] that focuses on the learning of the best sampling operator in the Fourier domain adapted to spectral properties of training set of images. We formulate the problem as a reconstruction from a finite number of sparse samples with a prior learned from the external dataset or learned on-fly for the image to be reconstructed. The proposed methods are tested on diverse datasets covering facial images, medical and multi-band astronomical applications using the mean square error and SSIM as a perceptual measure of reconstruction. The obtained results demonstrate some interesting properties of proposed methods that might be of interest for future research and extensions.

Keywords

  • astronomical image processing, computerised tomography, face recognition, gamma-ray bursts, image reconstruction, image sampling, learning (artificial intelligence), mean square error methods, frequency domain, pixel domain, high quality images, Square Kilometre Array, magnetic resonance imaging, computerized tomography, learnable compressive subsampling, mean square error, facial images, external dataset, sparse samples, finite number, spectral properties, Fourier domain, sampling operator, Training, Image reconstruction, Transforms, Image coding, Europe, Signal processing, Imaging, Compressive sensing, support learning, reconstruction, deep priors

Publication forum classification

Field of science, Statistics Finland