Convolutional sparse coding for high dynamic range imaging

Ana Serrano, Felix Heide, Diego Gutierrez, Gordon Wetzstein, Belen Masia

Research output: Contribution to journalArticle

45 Scopus citations

Abstract

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially-varying pixel exposures. In this paper, we propose a novel algorithm to recover high-quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently-introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher-quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.

Original languageEnglish (US)
Pages (from-to)153-163
Number of pages11
JournalComputer Graphics Forum
Volume35
Issue number2
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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