Can we beat Hadamard multiplexing? Data driven design and analysis for computational imaging systems

Abstract

Computational Imaging (CI) systems that exploit optical multiplexing and algorithmic demultiplexing have been shown to improve imaging performance in tasks such as motion deblurring, extended depth of field, light field and hyper-spectral imaging. Design and performance analysis of many of these approaches tend to ignore the role of image priors. It is well known that utilizing statistical image priors significantly improves demultiplexing performance. In this paper, we extend the Gaussian Mixture Model as a data-driven image prior (proposed by Mitra et. al [21]) to under-determined linear systems and study compressive CI methods such as light-field and hyper-spectral imaging. Further, we derive a novel algorithm for optimizing multiplexing matrices that simultaneously accounts for (a) sensor noise (b) image priors and (c) CI design constraints. We use our algorithm to design data-optimal multiplexing matrices for a variety of existing CI designs, and we use these matrices to analyze the performance of CI systems as a function of noise level. Our analysis gives new insight into the optimal performance of CI systems, and how this relates to the performance of classical multiplexing designs such as Hadamard matrices.

Multiplexed acquisition of bidirectional texture functions for materials

“…Mitra et al 5 take this even further and compute illumination patterns using an optimization based on image priors. In contrast to previous methods, their method is able to handle large amounts of light, but it still relies on the assumption of a one-dimensional affine noise model.…”

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes

“…Contrary to compressive computational imaging, MSFA offers a very time efficient option for spectral reconstruction. Recently, it has been shown that, when using wide-band filters, MSFA based methods can be more accurate in the common case where the “photon to read noise ratios” (PRR) is not very low [ 11 ]. Therefore, various multi-channel spectral imaging system with MSFA have been designed recently.…”

Spatio-spectral uniform multi-frame coded apertures for compressive spectral imaging

“…The coded apertures for super-resolution on the other hand, permit high resolution reconstructions from a low-resolution compressive measurement set. A data driven coding scheme is introduced in [8], in which a prior model of the scene is used, yet it has not been tested in CASSI. In this paper, a new family of coded aperture patterns named the SUM codes is introduced.…”