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.

Time-resolved multispectral imaging has many applications in different fields, which range from characterization of biological tissues to environmental monitoring. In particular, optical techniques, such as lidar and fluorescence lifetime imaging, require imaging at the subnanosecond scales over an extended area. In this paper, we demonstrate experimentally a time-resolved multispectral acquisition scheme based on single-pixel imaging. Single-pixel imaging is an emerging paradigm that provides low-cost high-quality images. Here, we use an adaptive strategy that allows acquisition and image reconstruction times to be reduced drastically or full basis scans. Adaptive time-resolved multispectral imaging scheme can have significant applications in biological imaging, at scales from macroscopic to microscopic.

A single-pixel camera is a computational imaging device that only requires a single point detector to capture the image of a scene. This device measures the inner product of the scene and the spatial light modulator patterns. The image of the scene can be recovered through post-processing the measurements obtained for a set of different patterns. Independent of the strategy used for image recovery, real acquisitions require the spatial light modulator patterns to be positive. In addition, the dark current measured in the absence of modulation must be rejected. To date, both experimental issues have been addressed empirically. In this paper, we solve these from a general perspective. Indeed, we propose to seek positive patterns that are linear combinations of the desired patterns (with negative values), and the linear transformation matrices are chosen to reject the dark current. We refer to the problem of finding the positive patterns and the linear combinations as 'pattern generalization'. To the best of our knowledge, this is the first time that this problem has been introduced. In addition, we show that pattern generalization can be solved using a semi nonnegative matrix factorization algorithm. The data obtained from simulations demonstrate that our approach performs similarly to or better than conventional methods, while using fewer measurements.

Purpose To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in MR renographic data and to examine the efficiency of a semi-automatic software package in processing renographic data from a cohort of clinical patients. Materials and Methods The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate GFR, RPF, and MTT from 36 patients who underwent free-breathing MR renography at 3T using saturation-recovery turbo-FLASH. Processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Inter-reader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine. Results The time taken to process one patient’s data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Inter-reader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05). Conclusion These results suggest that the proposed software can process MR renography data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions.

Single pixel imaging opened the door to a cheaper camera architecture able to operate in a wide spectral range. Compressive sensing has been used with such an optical setup to reconstruct an image using ℓ1-minimization. To avoid this type of reconstruction, we consider an adaptive approach leading to a direct restoration of an image and for which we propose a new acquisition strategy. Our technique allows one to acquire an image in the wavelet domain with a progressive non-linear acquisition strategy. This scheme is based on the non-linear approximation of the wavelet transform which takes advantage of the transformation's sparsity. This approximation is applied in a multiresolution way and is shown to offer high compression performance on simulated data. One application of the single pixel camera concerns time-resolved acquisition to observe fluorescence lifetime images of biological structures.

Single pixel imaging opened the door to a cheaper camera architecture able to operate in a wide spectral range. Such an optical setup has been used with compressed sensing to reconstruct an image via l1-minimization ruling out real time applications. In order to have a direct restoration of the image, we consider an adaptive approach for which we propose a new acquisition strategy. Our method progressively acquires an image in the wavelet domain by predicting the significant coefficients. For this, we base our technique on the non-linear approximation of the wavelet transform taking advantage of the transformation's sparsity. This new strategy is shown to offer high performance on simulated and real data that we compare to compressive sensing acquisitions. One possible application of the single pixel camera can be foreseen in fluorescence images of biological structures

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