Architecture for one-shot compressive imaging using computer-generated holograms.

We propose a synchronous implementation of compressive imaging. This method is mathematically equivalent to prevailing sequential methods, but uses a static holographic optical element to create a spatially distributed spot array from which the image can be reconstructed with an instantaneous measurement. We present the holographic design requirements and demonstrate experimentally that the linear algebra of compressed imaging can be implemented with this technique. We believe this technique can be integrated with optical metasurfaces, which will allow the development of new compressive sensing methods.Engineering and Physical Sciences Research Council (EPSRC) (EP/G037256/1, EP/L015455/1)

Compressive sampling using a pushframe camera

The recently described pushframe imager, a parallelized single pixel camera capturing with a pushbroom-like motion, is intrinsically suited to both remote-sensing and compressive sampling. It optically applies a 2D mask to the imaged scene, before performing light integration along a single spatial axis, but previous work has not made use of the architecture's potential for taking measurements sparsely. In this paper we develop a strongly performing static binarized noiselet compressive sampling mask design, tailored to pushframe hardware, allowing both a single exposure per motion time-step, and retention of 2D correlations in the scene. Results from simulated and real-world captures are presented, with performance shown to be similar to that of immobile — and hence inappropriate for satellite use — whole-scene imagers. A particular feature of our sampling approach is that the degree of compression can be varied without altering the pattern, and we demonstrate the utility of this for efficiently storing and transmitting multi-spectral images

Compressed sensing for enhanced through-the-wall radar imaging

Through-the-wall radar imaging (TWRI) is an emerging technology that aims to capture scenes behind walls and other visually opaque materials. The abilities to sense through walls are highly desirable for both military and civil applications, such as search and rescue missions, surveillance, and reconnaissance. TWRI systems, however, face with several challenges including prolonged data acquisition, large objects, strong wall clutter, and shadowing effects, which limit the radar imaging performances and hinder target detection and localization

COMPRESSIVE IMAGING AND DUAL MOIRE´ LASER INTERFEROMETER AS METROLOGY TOOLS

Metrology is the science of measurement and deals with measuring different physical aspects of objects. In this research the focus has been on two basic problems that metrologists encounter. The first problem is the trade-off between the range of measurement and the corresponding resolution; measurement of physical parameters of a large object or scene accompanies by losing detailed information about small regions of the object. Indeed, instruments and techniques that perform coarse measurements are different from those that make fine measurements. This problem persists in the field of surface metrology, which deals with accurate measurement and detailed analysis of surfaces. For example, laser interferometry is used for fine measurement (in nanometer scale) while to measure the form of in object, which lies in the field of coarse measurement, a different technique like moire technique is used. We introduced a new technique to combine measurement from instruments with better resolution and smaller measurement range with those with coarser resolution and larger measurement range. We first measure the form of the object with coarse measurement techniques and then make some fine measurement for features in regions of interest. The second problem is the measurement conditions that lead to difficulties in measurement. These conditions include low light condition, large range of intensity variation, hyperspectral measurement, etc. Under low light condition there is not enough light for detector to detect light from object, which results in poor measurements. Large range of intensity variation results in a measurement with some saturated regions on the camera as well as some dark regions. We use compressive sampling based imaging systems to address these problems. Single pixel compressive imaging uses a single detector instead of array of detectors and reconstructs a complete image after several measurements. In this research we examined compressive imaging for different applications including low light imaging, high dynamic range imaging and hyperspectral imaging

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorTese (Doutorado)Super-resolução de imagens é definido como urna classe de técnicas que melhora a resolução espacial de imagens. Métodos de super-resolução podem ser subdivididos em métodos para urna única imagens e métodos para múltiplas imagens. Esta tese foca no desenvolvimento de algoritmos baseados em teorias matemáticas para problemas de super-resolução de urna única imagem. Com o propósito de estimar urna imagem de saída, nós adotamos urna abordagem mista, ou seja: nós usamos dicionários de patches com restrição de esparsidade (método baseado em aprendizagem) e termos de regularização (método baseado em reconstrução). Embora os métodos existentes sejam eficientes, eles nao levam em consideração a geometria dos dados para: regularizar a solução, clusterizar os dados (dados sao frequentemente clusterizados usando algoritmos com a distancia Euclideana como métrica de dissimilaridade), aprendizado de dicionários (eles sao frequentemente treinados usando PCA ou K-SVD). Portante, os métodos do estado da arte ainda tem algumas deficiencias. Neste trabalho, nós propomos tres métodos originais para superar estas deficiencias. Primeiro, nós desenvolvemos SE-ASDS (um termo de regularização baseado em structure tensor) afim de melhorar a nitidez das bordas das imagens. SE-ASDS alcança resultados muito melhores que os algoritmos do estado da arte. Em seguida, nós propomos os algoritmos AGNN e GOC para determinar um subconjunto de amostras de treinamento a partir das quais um bom modelo local pode ser calculado para reconstruir urna dada amostra de entrada considerando a geometria dos dados. Os métodos AGNN e GOC superamos métodos spectral clustering, soft clustering e os métodos baseados em distancia geodésica na maioria dos casos. Depois, nós propomos o método aSOB que leva em consideração a geometria dos dados e o tamanho do dicionário. O método aSOB supera os métodos PCA e PGA. Finalmente, nós combinamos todos os métodos que propomos em um único algoritmo, a saber, G2SR. Nosso algoritmo G2SR mostra resultados melhores que os métodos do estado da arte em termos de PSRN, SSIM, FSIM e qualidade visual

Digitally-Assisted Mixed-Signal Wideband Compressive Sensing

Digitizing wideband signals requires very demanding analog-to-digital conversion (ADC) speed and resolution specifications. In this dissertation, a mixed-signal parallel compressive sensing system is proposed to realize the sensing of wideband sparse signals at sub-Nqyuist rate by exploiting the signal sparsity. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) front-end, which not only can filter out the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. Moreover, the signal randomization in the system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth rather than concentrate on a few frequencies as it is the case for a conventional ADC. This important new property relaxes the ADC SFDR requirement when sensing frequency-domain sparse signals. The mixed-signal compressive sensing system performance is greatly impacted by the accuracy of analog circuit components, especially with the scaling of CMOS technology. In this dissertation, the effect of the circuit imperfection in the mixed-signal compressive sensing system based on the PSCS front-end is investigated in detail, such as the finite settling time, the timing uncertainty and so on. An iterative background calibration algorithm based on LMS (Least Mean Square) is proposed, which is shown to be able to effectively calibrate the error due to the circuit nonideal factors. A low-speed prototype built with off-the-shelf components is presented. The prototype is able to sense sparse analog signals with up to 4 percent sparsity at 32 percent of the Nqyuist rate. Many practical constraints that arose during building the prototype such as circuit nonidealities are addressed in detail, which provides good insights for a future high-frequency integrated circuit implementation. Based on that, a high-frequency sub-Nyquist rate receiver exploiting the parallel compressive sensing is designed and fabricated with IBM90nm CMOS technology, and measurement results are presented to show the capability of wideband compressive sensing at sub-Nyquist rate. To the best of our knowledge, this prototype is the first reported integrated chip for wideband mixed-signal compressive sensing. The proposed prototype achieves 7 bits ENOB and 3 GS/s equivalent sampling rate in simulation assuming a 0.5 ps state-of-art jitter variance, whose FOM beats the FOM of the high speed state-of-the-art Nyquist ADCs by 2-3 times. The proposed mixed-signal compressive sensing system can be applied in various fields. In particular, its applications for wideband spectrum sensing for cognitive radios and spectrum analysis in RF tests are discussed in this work