Low power compressive sensing for hyperspectral imagery

Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.info:eu-repo/semantics/publishedVersio

Una comparación cuantitativa y cualitativa de análisis de rendimiento de las cámaras espectrales compresiva

Context: Spectral images (SI) contain spatial-spectral information about a scene arranging in a data cube, which often comprises a significant amount of data. However, traditional (SI) systems acquire data ignoring the high correlation between the measurements and the samples are redundant. Compressive spectral imaging systems compress spectral data in the acquisition step, so it allows reducing redundancy and the data amount. Recently, several spectral imaging systems have become available, providing new functionality for users and opening up the field to a wide array of new applications. For instance, the CASSI, SCSI, SSCS, and HYCA systems are four of the most outstanding systems.Methods: Some review works have provided comprehensive surveys of the available technologies and have shown how the new capabilities of spectral imaging approaches can be utilized. However, selecting a specific architecture requires a quantitative and qualitative comparison of these systems in the same scenarios.Results: This paper analyzes the qualitative and quantitative performance of these four compressive spectral imaging systems to evaluate them in the same scenarios. For that, the architectures are modeled as a system of linear equations; then, image reconstructions are accomplished through the same optimization approach, transmittance, coded aperture, and shot numbers.Conclusion: Results show that the performance of the SSCSI system attains better quality reconstruction in terms of PSNR.Contexto: Las imágenes espectrales (SI) contienen información espacio-espectral acerca de una escena disponible en un cubo de datos que usualmente comprende una cantidad significativa de éstos. Los sistemas tradicionales de (SI) adquieren datos redundantes ignorando la alta correlación entre las mediciones y las muestras redundantes. Los sistemas de compresión de imágenes espectrales comprimen los datos espectrales en la etapa de adquisición, lo que permite reducir la cantidad de datos y la redundancia. Actualmente, existen varios sistemas de imágenes espectrales disponibles que proporcionan nuevas funciones para los usuarios y abren un amplio campo de nuevas aplicaciones. Por ejemplo, los sistemas de CASSI, SCSI, SSCS, y HYCA son cuatro de los más destacados.Método: La revisión de algunos trabajos provee amplios estudios de tecnologías disponibles y muestra cómo se pueden utilizar las nuevas capacidades de los enfoques de formación de imágenes espectrales. Sin embargo, para la selección de una arquitectura específica se requiere una comparación cuantitativa y cualitativa de estos sistemas en los mismos escenarios.Resultados: En este trabajo se analiza el rendimiento cualitativo y cuantitativo de estos cuatro sistemas de compresión de imágenes espectrales para evaluarlos en los mismos escenarios. Para ello, cada arquitectura se modela como un sistema de ecuaciones lineales y el proceso de reconstrucción de las imágenes se logra con el mismo enfoque de optimización transmitancia, código de apertura y número de proyecciones.Conclusión: Se muestra que el sistema SSCSI alcanza el mejor rendimiento en la reconstrucción con el valor más alto PSNR

COMPRESSIVE SENSING APPROACH TO HYPERSPECTRAL IMAGE COMPRESSION

Hyperspectral image (HSI) processing is one of the key processes in satellite imaging applications. Hyperspectral imaging spectrometers collect huge volumes of data since the image is captured across different wavelength bands in the electromagnetic spectrum. As a result, compression of hyperspectral images is one of the active area in research community from many years. The research work proposes a new compressive sensing based approach for the compression of hyperspectral images called SHSIR (Sparsification of hyperspectral image and reconstruction). The algorithm computes the coefficients of fractional abundance map in matrix setup, which is used to reconstruct the hyperspectral image. To optimize the problem with non-smooth term existence along with large dimensionality, Bregman iterations method of multipliers is used, which converts the difficult optimization problem into simpler cyclic sequence problem. Experimental result demonstrates the supremacy of the proposed method over other existing techniques

High-quality hyperspectral reconstruction using a spectral prior

We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder, which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website

A Variable Density Sampling Scheme for Compressive Fourier Transform Interferometry

Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscopy. However, the effective resolution of FTI is limited by the durability of biological elements when exposed to illuminating light. Overexposed elements are subject to photo-bleaching and become unable to fluoresce. In this context, the acquisition of biological HS volumes based on sampling the Optical Path Difference (OPD) axis at Nyquist rate leads to unpleasant trade-offs between spectral resolution, quality of the HS volume, and light exposure intensity. We propose two variants of the FTI imager, i.e., Coded Illumination-FTI (CI-FTI) and Structured Illumination FTI (SI-FTI), based on the theory of compressive sensing (CS). These schemes efficiently modulate light exposure temporally (in CI-FTI) or spatiotemporally (in SI-FTI). Leveraging a variable density sampling strategy recently introduced in CS, we provide near-optimal illumination strategies, so that the light exposure imposed on a biological specimen is minimized while the spectral resolution is preserved. Our analysis focuses on two criteria: (i) a trade-off between exposure intensity and the quality of the reconstructed HS volume for a given spectral resolution; (ii) maximizing HS volume quality for a fixed spectral resolution and constrained exposure budget. Our contributions can be adapted to an FTI imager without hardware modifications. The reconstruction of HS volumes from CS-FTI measurements relies on an $l_1$-norm minimization problem promoting a spatiospectral sparsity prior. Numerically, we support the proposed methods on synthetic data and simulated CS measurements (from actual FTI measurements) under various scenarios. In particular, the biological HS volumes can be reconstructed with a three-to-ten-fold reduction in the light exposure.Comment: 45 pages, 11 figure

Design and simulation of compressive snapshot multispectral imaging system

Compressive Snapshot Spectral Imaging combines compressive sensing and snapshot spectral imaging (SSI) for restoring the image of the scene in both spatial and spectral contexts by using only a fewer number of sampling measurements of the captured image under the sparsity assumption. SSI is often realised through a coded aperture mask together with a single dispersive element as the main spatial modulator to implement compressive sampling. As one of the representative frameworks in this field, Coded Aperture Snapshot Spectral Imagers (CASSI) has prototyped a low-cost, compact platform to achieve compressive snapshot spectral imaging in the recent decade. Active research in the field includes advanced de-compressive recovery algorithms and also the employment of more sophisticated optical hardware for the design of more robust SSI system. This research addresses more of the latter direction and it focuses on how the CASSI framework can be further developed for various applications such as magnetic resonance imaging for medical diagnosis, enhancement of radar imaging system, facial expression detection and recognition, digital signal processing with sparse structure in terms of image denoising, image super-resolution and image classification. This thesis presents a summary of the research conducted over the past 4 years about the basic property of the CASSI system, which leads to the development of the spectral tuneable SSI design proposed during the course of the PhD study. This new design utilises a Dual-Prism assembly to embed the capability of wavelength-tuning without physically changing its optical elements. This Dual-Prism CASSI (DP-CASSI) adapts to dynamic environments far better than all the CASSI types of imagers published in the open domain which only function for a fixed set of wavelengths. This piece of work has been vii accepted by journal papers for publication. Other contributions of this research has been the enhancement of the Single-Prism (SP-CASSI) architecture and to produce a snapshot system with less aberration and better image quality than that published in the open domain. Moreover, the thesis also provides information about optical design of four different types of CASSI with slightly in-depth analysis about their optical system constructions, optical evaluations of system structure and their dispersive capabilities as the background of this research. Then a more detailed description of the proposed DP-CASSI with respected to its design and performance evaluation particularly its dispersion characteristics and the effects of system resolutions, are given. System verifications were conducted through ray-tracing simulation in three-dimension visualisation environments and the spectral characteristics of the targets are compared with that of the ground truth. The spectral tuning of the proposed DP-CASSI is achieved by adjusting the air gap displacement of dual-prism assembly. Typical spectral shifts of about 5 nm at 450 mm and 10 nm at 650 nm wavelength have been achieved in the present design when the air gap of the dual-prism is changed from 3.44 mm to 5.04 mm. The thesis summaries the optical designs, the performance and the pros and cons of the DP-CASSI syste

Parallelisation of greedy algorithms for compressive sensing reconstruction

Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as it is captured. The process of capturing and simultaneously compressing the signal is represented as linear sampling, which can encompass a variety of physical processes or signal processing. Instead of explicitly identifying redundancies in the source signal, CS relies on the property of sparsity in order to reconstruct the compressed signal. While linear sampling is much less burdensome than conventional compression, this is more than made up for by the high computational cost of reconstructing a signal which has been captured using CS. Even when using some of the fastest reconstruction techniques, known as greedy pursuits, reconstruction of large problems can pose a significant burden, consuming a great deal of memory as well as compute time. Parallel computing is the foundation of the field of High Performance Computing (HPC). Modern supercomputers are generally composed of large clusters of standard servers, with a dedicated low-latency high-bandwidth interconnect network. On such a cluster, an appropriately written program can harness vast quantities of memory and computational power. However, in order to exploit a parallel compute resource, an algorithm usually has to be redesigned from the ground up. In this thesis I describe the development of parallel variants of two algorithms commonly used in CS reconstruction, Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP), resulting in the new distributed compute algorithms DistMP and DistOMP. I present the results from experiments showing how DistMP and DistOMP can utilise a compute cluster to solve CS problems much more quickly than a single computer could alone. Speed-up of as much as a factor of 76 is observed with DistMP when utilising 210 workers across 14 servers, compared to a single worker. Finally, I demonstrate how DistOMP can solve a problem with a 429GB equivalent sampling matrix in as little as 62 minutes using a 16-node compute cluster.Funded by an ICASE award from the Engineering and Physical Sciences Research Council, with sponsorship provided by Thales Research and Technology