arXiv subjects

Alankar Kotwal

Publications and source records attributed to Alankar Kotwal.

4 recordsLinked to original sources

arXiv2023

Swept-Angle Synthetic Wavelength Interferometry

We present a new imaging technique, swept-angle synthetic wavelength interferometry, for full-field micron-scale 3D sensing. As in conventional synthetic wavelength interferometry, our technique uses light consisting of two narrowly-separated optical wavelengths, resulting in per-pixel interferometric measurements whose phase encodes scene depth. Our technique additionally uses a new type of light source that, by emulating spatially-incoherent illumination, makes interferometric measurements insensitive to aberrations and (sub)surface scattering, effects that corrupt phase measurements. The resulting technique combines the robustness to such corruptions of scanning interferometric setups, with the speed of full-field interferometric setups. Overall, our technique can recover full-frame depth at a lateral and axial resolution of 5 microns, at frame rates of 5 Hz, even under strong ambient light. We build an experimental prototype, and use it to demonstrate these capabilities by scanning a variety of objects, including objects representative of applications in inspection and fabrication, and objects that contain challenging light scattering effects.

cs.CV↗

arXiv2023

Passive Micron-scale Time-of-Flight with Sunlight Interferometry

We introduce an interferometric technique for passive time-of-flight imaging and depth sensing at micrometer axial resolutions. Our technique uses a full-field Michelson interferometer, modified to use sunlight as the only light source. The large spectral bandwidth of sunlight makes it possible to acquire micrometer-resolution time-resolved scene responses, through a simple axial scanning operation. Additionally, the angular bandwidth of sunlight makes it possible to capture time-of-flight measurements insensitive to indirect illumination effects, such as interreflections and subsurface scattering. We build an experimental prototype that we operate outdoors, under direct sunlight, and in adverse environment conditions such as machine vibrations and vehicle traffic. We use this prototype to demonstrate, for the first time, passive imaging capabilities such as micrometer-scale depth sensing robust to indirect illumination, direct-only imaging, and imaging through diffusers.

cs.CV↗

arXiv2017

Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery

There exist several applications in image processing (eg: video compressed sensing [Hitomi, Y. et al, "Video from a single coded exposure photograph using a learned overcomplete dictionary"] and color image demosaicing [Moghadam, A. A. et al, "Compressive Framework for Demosaicing of Natural Images"]) which require separation of constituent images given measurements in the form of a coded superposition of those images. Physically practical code patterns in these applications are non-negative, systematically structured, and do not always obey the nice incoherence properties of other patterns such as Gaussian codes, which can adversely affect reconstruction performance. The contribution of this paper is to design code patterns for video compressed sensing and demosaicing by minimizing the mutual coherence of the matrix $\boldsymbol{ΦΨ}$ where $\boldsymbolΦ$ represents the sensing matrix created from the code, and $\boldsymbolΨ$ is the signal representation matrix. Our main contribution is that we explicitly take into account the special structure of those code patterns as required by these applications: (1)~non-negativity, (2)~block-diagonal nature, and (3)~circular shifting. In particular, the last property enables for accurate and seamless patch-wise reconstruction for some important compressed sensing architectures.

cs.CV↗

arXiv2017

Optimizing Matrices For Compressed Sensing Using Existing Goodness Measures: Negative Results, And An Alternative

The bound that arises out of sparse recovery analysis in compressed sensing involves input signal sparsity and some property of the sensing matrix. An effort has therefore been made in the literature to optimize sensing matrices for optimal recovery using this property. We discover, in the specific case of optimizing codes for the CACTI camera, that the popular method of mutual coherence minimization does not produce optimal results: codes designed to optimize effective dictionary coherence often perform worse than random codes in terms of mean squared reconstruction error. This surprising phenomenon leads us to investigate the reliability of the coherence bound for matrix optimization, in terms of its looseness. We examine, on simulated data, the looseness of the bound as it propagates across various steps of the inequalities in a derivation leading to the final bound. We then similarly examine an alternate bound derived by Tang, G. et al, based on the $\ell_1/\ell_{\infty}$ notion of sparsity, which is a compromise between coherence and the restricted isometry constant (RIC). Moreover, we also perform a bound looseness analysis for the RIC as derived by Cai, T. et al. The conclusion of these efforts is that coherence optimization is problematic not only because of the coherence bound on the RIC, but also the RIC bound itself. These negative results imply that despite the success of previous work in designing sensing matrices based on optimization of a matrix quality factor, one needs to exercise caution in using them for practical sensing matrix design. We then introduce a paradigm for optimizing sensing matrices that overcomes the looseness of compressed sensing upper bounds using an average case error approach. We show a proof-of-concept design using this paradigm that performs convincingly better than coherence-based design in the CACTI case, and no worse for general matrices.

cs.IT↗