Convolutional Sparse Coding for High Dynamic Range Imaging

TL;DR

This paper introduces a novel convolutional sparse coding algorithm for high dynamic range imaging from a single coded exposure, outperforming existing methods and demonstrated through a prototype camera.

Contribution

It presents a practical convolutional sparse coding approach for HDR imaging from a single exposure, including hardware implementation.

Findings

Higher-quality HDR images than alternative methods

Evaluation of optical coding schemes and algorithm parameters

Prototype coded HDR camera demonstrating real-world utility

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.