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Many applications rely on thermal imagers to complement or replace visible light sensors in difficult imaging conditions. Recent advances in machine learning have opened the possibility of analyzing or enhancing images, yet these methods require large annotated databases. Training approaches that leverage data augmentation via simulated and synthetically-generated images could offer promising prospects. Here, we report on a method that uses generative adversarial nets (GANs) to synthesize images of a complementary contrast. Starting from a dual-modality dataset of co-registered visible and thermal images, we trained a GAN to generate synthetic thermal images from visible images and vice versa. Our results show that the procedure yields sharp synthesized images that might be used to augment dual-modality datasets or assist in visual interpretation, yet are also subject to the limitations imposed by contrast independence between thermal and visible images.

Generalized sampling is a flexible framework for signal acquisition, which relaxes the need for ideal pre-filters. Nevertheless, implementation remains challenging for dynamic imaging applications because it requires simultaneously measuring multiple overlapping inner-products and because only positive signals (intensities) can be measured by cameras. We present a method to collect videos of monochromatic objects by projecting the incoming signal at each pixel in a temporal B-spline space of degree 0, 1, or 2 by using a conventional RGB camera and a modulated three-color light source for illumination. Specifically, we solve the basis function overlap problem by multiplexing the acquisition in different color ranges and use B-spline pieces (which are positive) as projection kernels of a biorthogonal projection-expansion bases pair. The steps to recover signal samples include spectral unmixing and inverse filtering. Reconstructions we obtained from simulated and experimentally-acquired microscopy data demonstrate the feasibility of our approach.

3D deconvolution in optical wide eld microscopy aims at recovering optical sections through thick objects. Acquiring data from multiple, mutually-tilted directions helps ll the missing cone of information in the optical transfer function, which normally renders the deconvolution problem particularly ill-posed. Here, we propose a fast-converging iterative deconvolution method for multi-angle deconvolution microscopy. Specically, we formulate the imaging problem using a lter-bank structure, and present a multi-channel variation of a thresholded Landweber deconvolution algorithm with wavelet-sparsity regularization. Decomposition of the minimization problem into subband-dependent terms ensures fast convergence. We demonstrate the applicability of the algorithm via simulation results.

Digital holography plays an increasingly important role for biomedical imaging; it has particularly low invasiveness and allows quantitatively characterizing both amplitude and phase of propagating wave fronts. Fresnelets have been introduced as both a conceptual and practical tool to reconstruct digital holograms, simulate the propagation of monochromatic waves, or compress digital holograms. Propagating wavefronts that have a sparse representation in a traditional wavelet basis in their originating plane have a similarly sparse representation in propagation-distance-dependent Fresnelet bases. Although several applications have been reported, no implementation has been made widely available. Here we describe a Matlab-based Fresnelets toolbox that provides a set of user-friendly functions to implement the Fresnelet transform.

Multi-modal microscopy, such as combined bright-fiel and multi-color fluorescenc imaging, allows capturing a sample's anatomical structure, cell dynamics, and molecular activity in distinct imaging channels. However, only a limited number of channels can be acquired simultaneously and acquiring each channel sequentially at every time-point drastically reduces the achievable frame rate. Multi-modal imaging of rapidly moving objects (such as the beating embryonic heart), which requires high frame-rates, has therefore remained a challenge. We have developed a method to temporally register multimodal, high-speed image sequences of the beating heart that were sequentially acquired. Here we describe how maximizing the mutual information of time-shifted wavelet coefficien sequences leads to an implementation that is both accurate and fast. Specificall , we validate our technique on synthetically generated image sequences and show its effectiveness on experimental bright-fiel and fluorescenc image sequences of the beating embryonic zebrafis heart. This method opens the prospect of cardiac imaging in multiple channels at high speed without the need for multiple physical detectors.

Congenital cardiovascular defects are very common, occurring in 1% of live births, and cardiovascular failures are the leading cause of birth defect-related deaths in infants. To improve diagnostics, prevention and treatment of cardiovascular abnormalities, we need to understand not only how cells form the heart and vessels but also how physical factors such as heart contraction and blood flow influence heart development and changes in the circulatory network. Mouse models are an excellent resource for studying cardiovascular development and disease because of the resemblance to humans, rapid generation time, and availability of mutants with cardiovascular defects linked to human diseases. In this work, we present results on development and application of Doppler Swept Source Optical Coherence Tomography (DSS-OCT) for imaging of cardiovascular dynamics and blood flow in the mouse embryonic heart and vessels. Our studies demonstrated that the spatial and temporal resolution of the DSS-OCT makes it possible to perform sensitive measurements of heart and vessel wall movements and to investigate how contractile waves facilitate the movement of blood through the circulatory system.

The reconstruction of images from projections, diffraction fields, or other similar measurements requires applying signal processing techniques within a physical context. Although modeling of the acquisition procedure can conveniently be carried out in the continuous domain, actual reconstruction from experimental measurements requires the derivation of discrete algorithms that are accurate, efficient, and robust. In recent years, wavelets and multiresolution approaches have been applied successfully for common image processing tasks bridging the gap between discrete and continuous representations. We show that it is possible to express many physical problems in a wavelet framework, thereby allowing the derivation of efficient algorithms that take advantage of wavelet properties, such as multiresolution structure, sparsity, and space-frequency decompositions. We review several examples of such algorithms with applications to X-ray tomography, digital holography, and confocal microscopy and discuss possible future extensions to other modalities.

Three-dimensional information about an object, such as its depth, may be captured and stored digitally in a single, two-dimensional, real-valued hologram acquired in an off-axis geometry. Digital reconstruction of the hologram permits the quantitative retrieval of depth data and object position, or allows post-acquisition focusing on selected scenes. Over the past few decades, a number of reconstruction algorithms have been proposed to perform this task in various experimental conditions and for different purposes (metrology, imaging, etc.). Here, we aim at providing guidelines for deciding which algorithm to apply to a given problem. We evaluate reconstruction procedures based on criteria such as reconstruction quality and computational complexity. We propose a simulation procedure of the acquisition process, that allows us to compare a large body of experimental situations and, because the ground truth is known, achieve quantitative comparison.

With the availability of new confocal laser scanning microscopes, fast biological processes, such as the blood flow in living organisms at early stages of the embryonic development, can be observed with unprecedented time resolution. When the object under study has a periodic motion, e.g. a beating embryonic heart, the imaging capabilities can be extended to retrieve 4D data. We acquire nongated slice-sequences at increasing depth and retrospectively synchronize them to build dynamic 3D volumes. Here, we present a synchronization procedure based on the temporal correlation of wavelet features. The method is designed to handle large data sets and to minimize the influence of artifacts that are frequent in fluorescence imaging techniques such as bleaching, nonuniform contrast, and photon-related noise.

We present a zero-order and twin image elimination algorithm for digital Fresnel holograms that were acquired in an off-axis geometry. These interference terms arise when the digital hologram is reconstructed and corrupt the result. Our algorithm is based on the Fresnelet transform, a wavelet-like transform that uses basis functions tailor-made for digital holography. We show that in the Fresnelet domain, the coefficients associated to the interference terms are separated both spatially and with respect to the frequency bands. We propose a method to suppress them by selectively thresholding the Fresnelet coefficients. Unlike other methods that operate in the Fourier domain and affect the whole spacial domain, our method operates locally in both space and frequency, allowing for a more targeted processing.

We present a new class of wavelet bases—Fresnelets—which is obtained by applying the Fresnel transform operator to a wavelet basis of $ L _{ 2 } $ . The thus constructed wavelet family exhibits properties that are particularly useful for analyzing and processing optically generated holograms recorded on CCD-arrays. We first investigate the multiresolution properties (translation, dilation) of the Fresnel transform that are needed to construct our new wavelet. We derive a Heisenberg-like uncertainty relation that links the localization of the Fresnelets with that of the original wavelet basis. We give the explicit expression of orthogonal and semi-orthogonal Fresnelet bases corresponding to polynomial spline wavelets. We conclude that the Fresnel B-splines are particularly well suited for processing holograms because they tend to be well localized in both domains.

We consider using spline interpolation to improve the standard filtered back-projection (FBP) tomographic reconstruction algorithm. In particular, we propose to link the design of the filtering operator with the interpolation model that is applied to the sinogram. The key idea is to combine the ramp filtering and the spline fitting process into a single filtering operation. We consider three different approaches. In the first, we simply adapt the standard FBP for spline interpolation. In the second approach, we replace the interpolation by an oblique projection onto the same spline space; this increases the peak signal-noise ratio by up to 2.5 dB. In the third approach, we perform an explicit discretization by observing that the ramp filter is equivalent to a fractional derivative operator that can be evaluated analytically for splines. This allows for an exact implementation of the ramp filter and improves the image quality by an additional 0.2 dB. This comparison is unique as the first method has been published only for degree n = 0, whereas the two other methods are novel. We stress that the modification of the filter improve the reconstruction quality especially at low (faster) interpolation degrees (n = 1); the difference between the methods becomes marginal for cubic or higher degrees (n ≥ 3).

The combination of wavelength multiplexing and spectral interferometry allows for the encoding of multidimensional information and its transmission over a mono-dimensional channel; for example, measurements of a surface's topography acquired through a monomode fiber in a small endoscope. The local depth of the imaged object is encoded in the local spatial frequency of the signal measured at the output of the fiber-decoder system. We propose a procedure to retrieve the depth-map by determining the signal's instantaneous frequency. First, we compute its continuous, complex-valued, wavelet transform (CWT). The frequency signature at every position is contained in the resulting scalogram. We then extract the ridge of maximal response by use of a dynamic programming algorithm thus directly recovering the object's topography. We present results that validate this procedure based on both simulated and experimental data.