518 research outputs found
Approximate Message Passing in Coded Aperture Snapshot Spectral Imaging
We consider a compressive hyperspectral imaging reconstruction problem, where
three-dimensional spatio-spectral information about a scene is sensed by a
coded aperture snapshot spectral imager (CASSI). The approximate message
passing (AMP) framework is utilized to reconstruct hyperspectral images from
CASSI measurements, and an adaptive Wiener filter is employed as a
three-dimensional image denoiser within AMP. We call our algorithm
"AMP-3D-Wiener." The simulation results show that AMP-3D-Wiener outperforms
existing widely-used algorithms such as gradient projection for sparse
reconstruction (GPSR) and two-step iterative shrinkage/thresholding (TwIST)
given the same amount of runtime. Moreover, in contrast to GPSR and TwIST,
AMP-3D-Wiener need not tune any parameters, which simplifies the reconstruction
process.Comment: to appear in Globalsip 201
Side Information in Coded Aperture Compressive Spectral Imaging
Coded aperture compressive spectral imagers sense a three-dimensional cube by using two-dimensional projections of the coded and spectrally dispersed source. These imagers systems often rely on FPA detectors, SLMs, micromirror devices (DMDs), and dispersive elements. The use of the DMDs to implement the coded apertures facilitates the capture of multiple projections, each admitting a different coded aperture pattern. The DMD allows not only to collect the sufficient number of measurements for spectrally rich scenes or very detailed spatial scenes but to design the spatial structure of the coded apertures to maximize the information content on the compressive measurements. Although sparsity is the only signal characteristic usually assumed for reconstruction in compressing sensing, other forms of prior information such as side information have been included as a way to improve the quality of the reconstructions. This paper presents the coded aperture design in a compressive spectral imager with side information in the form of RGB images of the scene. The use of RGB images as side information of the compressive sensing architecture has two main advantages: the RGB is not only used to improve the reconstruction quality but to optimally design the coded apertures for the sensing process. The coded aperture design is based on the RGB scene and thus the coded aperture structure exploits key features such as scene edges. Real reconstructions of noisy compressed measurements demonstrate the benefit of the designed coded apertures in addition to the improvement in the reconstruction quality obtained by the use of side information
Coded Aperture Hyperspectral Image Reconstruction
This article belongs to the Special Issue Computational Spectral Imaging[Abstract] In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.This work was funded by the Xunta de Galicia (by Grant ED431C 2020/15 and Grant ED431G 2019/01 to support the Centro de Investigación de Galicia “CITIC”), the Agencia Estatal de Investigación of Spain (by Grants RED2018-102668-T and PID2019-104958RB-C42), and the ERDF funds of the EU (FEDER Galicia 2014-2020 and AEI/FEDER Programs, UE).Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0
Binarized Spectral Compressive Imaging
Existing deep learning models for hyperspectral image (HSI) reconstruction
achieve good performance but require powerful hardwares with enormous memory
and computational resources. Consequently, these methods can hardly be deployed
on resource-limited mobile devices. In this paper, we propose a novel method,
Binarized Spectral-Redistribution Network (BiSRNet), for efficient and
practical HSI restoration from compressed measurement in snapshot compressive
imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base
model to be binarized. Then we present the basic unit, Binarized
Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively
redistribute the HSI representations before binarizing activation and uses a
scalable hyperbolic tangent function to closer approximate the Sign function in
backpropagation. Based on our BiSR-Conv, we customize four binarized
convolutional modules to address the dimension mismatch and propagate
full-precision information throughout the whole network. Finally, our BiSRNet
is derived by using the proposed techniques to binarize the base model.
Comprehensive quantitative and qualitative experiments manifest that our
proposed BiSRNet outperforms state-of-the-art binarization methods and achieves
comparable performance with full-precision algorithms. Code and models are
publicly available at https://github.com/caiyuanhao1998/BiSCI and
https://github.com/caiyuanhao1998/MSTComment: NeurIPS 2023; The first work to study binarized spectral compressive
imaging reconstruction proble
An Introduction to Optimization Techniques in Computer Graphics
International audienceBackground: Many students in Computer Science do not have a sufficient background in applied mathematics to employ state-of-the-art optimization techniques and to judge the outcome of such techniques critically (e.g. regarding the stability/quality/accuracy of their output). At the same time, the use of optimization techniques in computer graphics is becoming ubiquitous. Treating optimization algorithms as a black box yields sub-optimal results at best. At worst, stability issues and convergence problems may prevent the solution of a problem or impede the general application of a method to a wide range of input, i.e. beyond the set of examples shown in a paper. The course will draw attention to these aspects and to current best practices. This will enable participants to judge articles that use optimization schemes critically and improve their own skill set
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