High Dynamic Range Imaging Using Deep Image Priors

Gauri Jagatap, Chinmay Hegde

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Traditionally, dynamic range enhancement for images has involved a combination of contrast improvement (via gamma correction or histogram equalization) and a denoising operation to reduce the effects of photon noise. More recently, modulo-imaging methods have been introduced for high dynamic range photography to significantly expand dynamic range at the sensing stage itself. The transformation function for both of these problems is highly non-linear, and the image reconstruction procedure is typically non-convex and ill-posed. A popular recent approach is to regularize the above inverse problem via a neural network prior (such as a trained autoencoder), but this requires extensive training over a dataset with thousands of paired regular/HDR image data samples.In this paper, we introduce a new approach for HDR image reconstruction using neural priors that require no training data. Specifically, we employ deep image priors, which have been successfully used for imaging problems such as denoising, super-resolution, inpainting and compressive sensing with promising performance gains over conventional regularization techniques. In this paper, we consider two different approaches to high dynamic range (HDR) imaging - gamma encoding and modulo encoding - and propose a combination of deep image prior and total variation (TV) regularization for reconstructing low-light images. We demonstrate the significant improvement achieved by both of these approaches as compared to traditional dynamic range enhancement techniques.

    Original languageEnglish (US)
    Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages9289-9293
    Number of pages5
    ISBN (Electronic)9781509066315
    DOIs
    StatePublished - May 2020
    Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
    Duration: May 4 2020May 8 2020

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2020-May
    ISSN (Print)1520-6149

    Conference

    Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
    CountrySpain
    CityBarcelona
    Period5/4/205/8/20

    Keywords

    • convolutional networks
    • Deep image prior
    • HDR imaging
    • inverse imaging
    • low-light enhancement
    • modulo camera
    • untrained neural networks

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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