A parallel compressive imaging architecture for one-shot acquisition

Abstract

Abstract-A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of reconstructed pixels and is able to acquire the image in a single acquisition. This paves the way for the development of video architectures that acquire several frames per second. We specifically address the diffraction problem, showing that deconvolution normally used to recover diffraction blur can be replaced by convolution of the sensing matrix, and how measurements of a 0/1 physical sensing matrix can be converted to -1/1 compressive sensing matrix without any extra acquisitions. Simulations of our architecture show that the image quality is comparable to that of a classic Compressive Imaging camera, whereas the proposed architecture avoids long acquisition times due to sequential sensing. This one-shot procedure also allows to employ a fixed sensing matrix instead of a complex device such as a Digital Micro Mirror array or Spatial Light Modulator. It also enables imaging at bandwidths where these are not efficient.

A non-convex blind calibration method for randomised sensing strategies

“…In particular (1) relates to computational sensing applications in which unknown d are associated to positive gains (see Figure 1) while p random matrix instances can be applied on a source x by a suitable (i.e., programmable) light modulation embodiment. This setup matches compressive imaging configurations [4], [10], [12]- [14] with an important difference in that the absence of a priori structure on (x, d) in (1) implies an over-Nyquist sampling regime with respect to (w.r.t.) n, i.e., exceeding the number of unknowns as mp ≥ n + m. When the effect of d is critical (i.e., assumingd ≈ I m would lead to Table I FINITE-SAMPLE AND EXPECTED VALUES OF THE OBJECTIVE FUNCTION; ITS GRADIENT COMPONENTS AND HESSIAN MATRIX; THE INITIALISATION…”

Multispectral Compressive Imaging Strategies Using Fabry–Pérot Filtered Sensors

“…As explained in [42], we can alter the CA pattern and measurements vector so that the symbols of S(u) effectively become S i ∈ {−1, 1} instead of S i ∈ {0, 1}. We propose to either use two complementary patterns S + (u) and S − (u), where transparent pixels (S i = 1) become opaque (S i = 0) and vice versa, and subtract the corresponding measurements vectors, y = y + − y − , or to subtract measurement made with a fully transparent aperture, S on (u) (i.e., S i = 1, ∀i), from 2y + , i.e., y = 2y + − y on .…”

“…3. This follows the ideas originally introduced by [42]. The difference, here, is that we use the FP filtered sensor instead of a panchromatic sensor.…”

Graded Quantization for Multiple Description Coding of Compressive Measurements

“…We will not consider this kind of approach in the paper because we want to explicitly avoid any preprocessing of the signal before acquisition. Indeed, compressive measurements could be directly obtained by specialized hardware (e.g., [4]- [6]), thus hindering any preprocessing of the data. Deng et al, [7] argue that the democratic property of random projections makes compressive sensing image coding robust to channel losses.…”