Convolutional Sparse Coding for High Dynamic Range Imaging

• Published in: Computer graphics forum Vol. 35; no. 2; pp. 153 - 163
• Main Authors: Serrano, Ana, Heide, Felix, Gutierrez, Diego, Wetzstein, Gordon, Masia, Belen
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2016
• Subjects: Algorithms; Analysis; Categories and Subject Descriptors (according to ACM CCS); Coding; Construction; Digitization and Image Capture; Exposure; I.4.1 [Image Processing and Computer Vision]; Image acquisition; Image processing systems; Image reconstruction; Imaging; Reconstruction; Sensors; Studies
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12819

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.