Abstract
Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.
Highlights
Imaging through scattering media has recently received a lot of attention
In this work we show that correlation image sensors such as photonic mixer devices [6, 7] can be effectively used for imaging in scattering and turbid media, when combined with computational analysis based on sparse coding (Fig. 1)
We demonstrated that correlation image sensors can be used for imaging in scattering and turbid media
Summary
Imaging through scattering media has recently received a lot of attention. While many works have considered microscopic settings such as imaging in biological tissue [1, 2], we consider here the macrosopic problem, with ultimate target applications such as underwater imaging or imaging through fog. For a single camera pixel, and under the assumption of a single light path with travel time with τ a contributing TtoextPhoisinpt ifxoenlt,stuhseeidlliunmEiMnaFt.ion at the camera is a phase shifted signal s(t) reduced ampRlietauddethdeuTeextoPosiunrtfmacaenuaalblebdeofoarendyogueodmeleettreicthfiaslbloofxf..: AAAcoArrelation image sensor such as a photonic mixer device integrates (exposes) over a large number (≈ 104–105). Correlation image sensors are operated in homodyne mode, i.e., ωT = ωR, in which case the measured correlation is given as p(φ ) = f s = F−1 (F(s)(ξ ) · F( f )(ξ ) This function is sampled by each correlation image pixel for different relative phase shifts φ. At a pixel a continuum of path lengths is measured, where only few ballistic photons directly hit objects submerged in the scattering media This strong scattering, which makes traditional imaging very challenging, can only be handled if multi path contributions are removed effectively
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