Adaptive Basis Scan by Wavelet Prediction for Single-Pixel Imaging

Abstract

Single pixel camera imaging is an emerging paradigm that allows high-quality images to be provided by a device only equipped with a single point detector. A single pixel camera is an experimental setup able to measure the inner product of the scene under view-the image-with any user-defined pattern. Post-processing a sequence of point measurements obtained with different patterns permits to recover spatial information, as it has been demonstrated by state-of-theart approaches belonging to the compressed sensing framework. In this paper, a new framework for the choice of the patterns is proposed together with a simple and efficient image recovery scheme. Our goal is to overcome the computationally demanding 1-minimization of compressed sensing. We propose to choose patterns among a wavelet basis in an adaptive fashion, which essentially relies onto the prediction of the significant wavelet coefficients' location. More precisely, we adopt a multiresolution strategy that exploits the set of measurements acquired at coarse scales to predict the set of measurements to be performed at a finer scale. Prediction is based on a fast cubic interpolation in the image domain. A general formalism is given so that any kind of wavelets can be used, which enables one to adjust the wavelet to the type of images related to the desired application. Both simulated and experimental results demonstrate the ability of our technique to reconstruct biomedical images with improved quality compared to CS-based recovery. Application to real-time fluorescence imaging of biological tissues could benefit from the proposed method.

Multiple-view diffuse optical tomography system based on time-domain compressive measurements

“…Future work will be devoted to the optimization of the data set (choice of illumination/detection patterns, number of views, and temporal gates) and system improvements (detection efficiency, calibration procedure) in order to strongly reduce the acquisition time, while preserving or even increasing the information content. In particular, adaptive basis scan approaches will be investigated [22].…”

Assessing patterns for compressive fluorescence lifetime imaging

“…The imaging performance depends on the choice of compression patterns as well as the strategy for selecting a subset for measurements [12]. Multiple basis patterns such as Hadamard [13], Fourier [14], speckle [15], and wavelet [16] have been proposed. Intensity imaging using Hadamard and Fourier patterns have been compared in Ref.…”

“…The x ∕ y frequencies change from 1 to 8 with eight phase shifts (see Visualization 5). Haar patterns are taken from a Haar wavelet basis that contains 16 scaling functions [16] (see Visualization 6). To investigate the performances of these various basis patterns in the case of monolifetime, we prepared fluorescence phantoms with RPI letters: R and I contained AF750 (ThermoFisher Scientific, A33085) fluorescent dye at 4.16 μM concentration prepared with PBS 1X; letter P contained HITCI (Sigma Aldrich, 252034) dye at 40 μM concentration prepared with 100% ethanol.…”

“…For all basis patterns, we considered increasing compression ratios (~50%, 60%, 70%, 80%, and 90%) by decreasing the number of patterns. While most approaches select patterns prior to acquisition [14], adaptive strategies select patterns during acquisition [16,24]. For Hadamard, Fourier, and Speckle basis, patterns were chosen a priori , and a linear solver reconstructed the images from the raw data.…”

Widefield compressive multiphoton microscopy

“…It is important to note that our proposed solution is inspired by an emerging area within the compressed sensing community known as adaptive sampling. Adaptive sampling techniques typically image a large FOV at low resolution using Fourier or wavelet basis scan in order to identify regions with significant information [22,23]. ROIS are subsequently scanned at finer resolutions for a higher quality image reconstruction.…”