Injecting Image Priors into Learnable Compressive Subsampling

Martino Ferrari, Olga Taran, Taras Holotyak, Karen Egiazarian, Slava Voloshynovskiy

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


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

    Publication series

    ISSN (Electronic)2076-1465


    ConferenceEuropean Signal Processing Conference
    Period1/01/00 → …


    • 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

    • Publication forum level 1


    Dive into the research topics of 'Injecting Image Priors into Learnable Compressive Subsampling'. Together they form a unique fingerprint.

    Cite this