Integral imaging acquisition and processing for visualization of photon counting images in the mid-wave infrared range

Penència presentada al SPIE Conference Volume 9867 "Three-Dimensional Imaging, Visualization, and Display 2016" organitzat per Bahram Javidi i Jung-Young Son i celebrat a Baltimore, (Maryland, United States) el 17 d' abril de 2016In this paper, we present an overview of our previously published work on the application of the maximum likelihood (ML) reconstruction method to integral images acquired with a mid-wave infrared detector on two different types of scenes: one of them consisting of a road, a group of trees and a vehicle just behind one of the trees (being the car at a distance of more than 200m from the camera), and another one consisting of a view of the Wright Air Force Base airfield, with several hangars and different other types of installations (including warehouses) at distances ranging from 600m to more than 2km. Dark current noise is considered taking into account the particular features this type of sensors have. Results show that this methodology allows to improve visualization in the photon counting domain

Three Dimensional Visualization of Long Range Scenes by Photon Counting Mid-Wave Infrared Integral Imaging

Integral Imaging under photon counting conditions has found different three-dimensional (3D) imaging applications, including 3D image reconstruction and recognition. In this letter, we present the application of the maximum likelihood (ML) estimation method for visualization of 3D scenes in photon starved environments using Mid-Wave Infrared 3D data of real scenes acquired at distances ranging from 50m to more than 2km. To the best of our knowledge, this is the first report on Mid-Wave Infrared 3D photon counting integral imaging of distant scenes

Single-frame super-resolution in remote sensing: a practical overview

Image acquisition technology is improving very fast from a performance point of view. However, there are physical restrictions that can only be solved using software processing strategies. This is particularly true in the case of super resolution (SR) methodologies. SR techniques have found a fertile application field in airborne and space optical acquisition platforms. Single-frame SR methods may be advantageous for some remote-sensing platforms and acquisition time conditions. The contributions of this article are basically two: (1) to present an overview of single-frame SR methods, making a comparative analysis of their performance in different and challenging remote-sensing scenarios, and (2) to propose a new single-frame SR taxonomy, and a common validation strategy. Finally, we should emphasize that, on the one hand, this is the first time, to the best of our knowledge, that such a review and analysis of single SR methods is made in the framework of remote sensing, and, on the other hand, that the new single-frame SR taxonomy is aimed at shedding some light when classifying some types of single-frame SR methods.This work was supported by the Spanish Ministry of Economy under the project ESP2013 - 48458-C4-3-P, by Generalitat Valenciana through project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09

Colour image denoising by eigenvector analysis of neighbourhood colour samples

[EN] Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.Latorre-Carmona, P.; Miñana, J.; Morillas, S. (2020). Colour image denoising by eigenvector analysis of neighbourhood colour samples. 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Latent topic-based super-resolution for remote sensing

This letter presents a novel single-image Super-Resolution (SR) approach based on latent topics specially designed to remote sensing imagery. The proposed approach pursues to super-resolve topics uncovered from low-resolution images instead of super-resolving image patches themselves. An experimental comparison is con- ducted using nine di ff erent SR methods over four aerial image data- sets. Experiments revealed the potential of latent topics in remote sensing SR by reporting that the proposed approach is able to provide a competitive advantage especially in low noise conditions.This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458- C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through project PROMETEO-II/2014/ 062, and by Universitat Jaume I through project P11B2014-09

Photon Counting 3-D Object Recognition Using Digital Holography

We present an analysis of the recognition performance of 3-D objects reconstructed from digital holograms recorded under photon counting conditions. The digital holograms are computed by applying four-step phase-shifting techniques to interferograms recorded with weak coherent light. Recognition capability is analyzed as a function of the total number of photons by using a maximum-likelihood approach adapted to one-class classification problems. The likelihood is modeled assuming a Gaussian distribution, whose centroid corresponds to the highest value in a mixture of two Gaussian values. The recognition capability is studied both in terms of the axial distance and the lateral position of the reconstructed 3-D object

Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions

A common assumption in the integral imaging reconstruction is that a pixel will be photo-consistent if all viewpoints observed by the different cameras converge at a single point when focusing at the proper depth. However, the presence of occlusions between objects in the scene prevents this from being fulfilled. In this paper, a novel depth and all-in focus image estimation method is presented, based on a photo-consistency measure that uses the median criterion in relation to the elemental images. The interest of this approach is to find a solution to detect which camera correctly sees the partially occluded object at a certain depth and allows for a precise solution to the object depth. In addition, a robust solution is proposed to detect the boundary limits between partially occluded objects, which are subsequently used during the regularization depth estimation process. The experimental results show that the proposed method outperforms other state-of-the-art depth estimation methods in a synthetic aperture integral imaging framework

Polarimetric identification of 3D-printed nano particle encoded optical codes

Document signature is a powerful technique used to determine whether a message is tampered or valid. Recently, this concept was extended to optical codes: we demonstrated that the combined use of optical techniques and machine learning algorithms might be able to distinguish among different classes of samples. In the present work, we produce nano particle encoded optical codes with predetermined designs synthesized with a 3D printer. We used conventional polylactic acid filament filled with metallic powder to include the effect of nano-encoding for unique polarimetric signatures. We investigated an interesting real-world scenario, that is, we demonstrate how a single class of codes is distinguished among a group of samples to be rejected. This is a difficult unbalanced problem since the number of polarimetric signatures that characterize the true class is small compared to the complete dataset. Each sample is characterized by analyzing the polarization state of the emerging light. Using the one class-support vector machine algorithm we found high accuracy figures in the recognition of the true class codes. To the best of our knowledge, this is the first report on implementing optical codes with nano particle encoded materials using 3D printing technology

Three-dimensional imaging with multiple degrees of freedom using data fusion

This paper presents an overview of research work and some novel strategies and results on using data fusion in 3-D imaging when using multiple information sources. We examine a variety of approaches and applications such as 3-D imaging integrated with polarimetric and multispectral imaging, low levels of photon flux for photon-counting 3-D imaging, and image fusion in both multiwavelength 3-D digital holography and 3-D integral imaging. Results demonstrate the benefits data fusion provides for different purposes, including visualization enhancement under different conditions, and 3-D reconstruction quality improvement

Real Time Automated Counterfeit Integrated Circuit Detection using X-ray Microscopy

Determining the authenticity of integrated circuits is paramount to preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. Existing techniques to distinguish between authentic and counterfeit integrated circuits (ICs) often include destructive testing requiring subject matter experts. We present a nondestructive technique to detect ICs using x-ray microscopy and advanced imaging analysis with different pattern recognition approaches. Our proposed method is completely automated, and runs in real time. In our approach, images of an integrated circuit are obtained from an x-ray microscope. Local binary pattern features are then extracted from the x-ray image, followed by dimensionality reduction through principal component analysis, and alternatively through a nonlinear principal component methodology using a stacked autoencoder embedded in a deep neural network. From the reduced dimension features, we train two types of learning machines, a support vector machine with a nonlinear kernel and a deep neural network. We present experiments using authentic and ICs to demonstrate that the proposed approach achieves an accuracy of 100% in distinguishing between the counterfeit and authentic samples.This work was supported by the NSF grant NSF/CISE Award #CNS–134427