Quantitative characterization of biofunctionalization layers by robust image analysis for biosensor applications

Abstract: This work describes the development of a characterization method for biofunctionalized surfaces and its use for biosensor applications. The method is based on the processing of fluorescence images obtained by confocal microscopy.

Compressive optical deflectometric tomography: a constrained total-variation approach

Abstract: Optical Deflectometric Tomography (ODT) provides an accurate characterization of transparent materials whose complex surfaces present a real challenge for manufacture and control. In ODT, the refractive index map (RIM) of a transparent object is reconstructed by measuring light deflection under multiple orientations.

From bits to images: Inversion of local binary descriptors

Abstract: Local Binary Descriptors are becoming more and more popular for image matching tasks, especially when going mobile. While they are extensively studied in this context, their ability to carry enough information in order to infer the original image is seldom addressed.

Heterogenous void growth revealed by in situ 3-D X-ray mocrotomography using automatic cavity tracking

Abstract: Ductile fracture by nucleation, growth and coalescence of internal voids is the dominant fracture mechanism in metals at ambient temperature. Micromechanics-based models for each elementary mechanism have been developed and enhanced over the past 40 years, allowing microstructure-informed failure predictions essentially assuming homogeneous damage evolution.

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Abstract: The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit-depth.

Stabilizing Nonuniformly Quantized Compressed Sensing with Scalar Companders

Abstract: This paper studies the problem of reconstructing sparse or compressible signals from compressed sensing measurements that have undergone nonuniform quantization. Previous approaches to this Quantized Compressed Sensing (QCS) problem based on Gaussian models (bounded l2-norm) for the quantization distortion yield results that, while often acceptable, may not be fully consistent: re-measurement and quantization of the reconstructed signal do not necessarily match the initial observations.

Analysis and experimental evaluation of Image-based PUFs

Abstract: Physically Unclonable Functions (PUFs) are becoming popular tools for various applications such as anti-counterfeiting schemes. The security of a PUF-based system relies on the properties of its underlying PUF. Usually, evaluating PUF properties is not simple as it involves assessing a physical phenomenon.

A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity

More information: This paper is part of the special issue “Advances in Multirate Filter Bank Structures and Multiscale Representations.” Here is a webpage related to this work (on Laurent Duval’s website).

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Abstract: In this paper, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program.