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

GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain

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

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Digital image blurring-deblurring process for improved storage and transmission

This paper investigates the likelihood of using a blurring-deblurring process as a pre-post-processing step in standard image reconstruction and compression. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a coded and compressed representation which renders it blurred and degraded. The compressibility of an image increases with the blurring, whereby the relation between compression ratio (CR) and the blurring scale is approximately linear. Hence, by pre-processing and blurring an image before compression, the CR will increase accordingly. The function or process tested here for blurring-deblurring an image is based on a pixel group processing, whereby the original image is sampled at sub-pixel levels. Since the sub-pixel shifts between each pixel group sampling are known exactly,a blurred image is created which can be shown to contain the details of the original image and thereby restored or reconstructed by reversing the blurring process.The complementary effects of increased CR are examined in terms of coding/decoding execution times and the quality of the reconstructed images