Digital imaging | EPFL Graph Search

Digital imaging or digital image acquisition is the creation of a digital representation of the visual characteristics of an object, such as a physical scene or the interior structure of an object. The term is often assumed to imply or include the , , , printing and display of such images. A key advantage of a , versus an analog image such as a film photograph, is the ability to digitally propagate copies of the original subject indefinitely without any loss of image quality. Digital imaging can be classified by the type of electromagnetic radiation or other waves whose variable attenuation, as they pass through or reflect off objects, conveys the information that constitutes the . In all classes of digital imaging, the information is converted by s into digital signals that are processed by a computer and made output as a visible-light image. For example, the medium of visible light allows digital photography (including digital videography) with various kinds of digital cameras (incl

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Hatef Otroshi Shahreza, Seyedeh Sahar Sadrizadeh

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as prior knowledge. Moreover, we define a multi-term loss function to train our network. In particular, we define a novel term to impose the sparsity nature of impulsive noise. Experimental results indicate that our deep learning approach significantly outperforms other state-of-the-art methods in terms of reconstruction quality and speed on a system equipped with GPU. Meanwhile, we introduce a fast iterative method, as a post-processing stage, to further improve the reconstruction quality of our neural network. The proposed post-processing algorithm improves the reconstruction quality in only a fraction of a second. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

ELSEVIER2022

Development of a systematic computer vision-based method to analyse and compare images of false identity documents for forensic intelligence purposes-Part I: Acquisition, calibration and validation issues

Marie Auberson, Simon Baechler, Thibault Genessay, Luc Patiny, Michaël Giuseppe Zasso

Following their detection and seizure by police and border guard authorities, false identity and travel documents are usually scanned, producing digital images. This research investigates the potential of these images to classify false identity documents, highlight links between documents produced by a same modus operandi or same source, and thus support forensic intelligence efforts. Inspired by previous research work about digital images of Ecstasy tablets, a systematic and complete method has been developed to acquire, collect, process and compare images of false identity documents. This first part of the article highlights the critical steps of the method and the development of a prototype that processes regions of interest extracted from images. Acquisition conditions have been fine-tuned in order to optimise reproducibility and comparability of images. Different filters and comparison metrics have been evaluated and the performance of the method has been assessed using two calibration and validation sets of documents, made up of 101 Italian driving licenses and 96 Portuguese passports seized in Switzerland, among which some were known to come from common sources. Results indicate that the use of Hue and Edge filters or their combination to extract profiles from images, and then the comparison of profiles with a Canberra distance-based metric provides the most accurate classification of documents. The method appears also to be quick, efficient and inexpensive. It can be easily operated from remote locations and shared amongst different organisations, which makes it very convenient for future operational applications. The method could serve as a first fast triage method that may help target more resource-intensive profiling methods (based on a visual, physical or chemical examination of documents for instance). Its contribution to forensic intelligence and its application to several sets of false identity documents seized by police and border guards will be developed in a forthcoming article (part II). (C) 2016 Elsevier Ireland Ltd. All rights reserved.

Elsevier Ireland Ltd2016

A novel microfabrication platform for hybrid multilayer MEMS

Nahid Hosseini

In Atomic force microscopy (AFM), the tip-sample interaction force can be measured through two primary detection techniques: optical beam detection (OBD) and electrical (self-sensing) readout. Compared to the optical method, the convenience of the self-sensing readout AFM measurements comes at the cost of higher force noise. In the self-sensing method, there is a trade-off between reducing the force noise and maintaining the cantilever characteristics (e.g. resonance frequency, spring constant, quality factor, and planar dimension) within the practical limits. The core of my research was the development of hybrid multilayer self-sensing cantilevers with up to one order-of-magnitude better force sensitivity than state-of-the-art silicon self-sensing cantilevers. Thanks to a material engineering approach combined with non-standard fabrication methods, the developed cantilevers are designed such that a polymer core is sandwiched between two hard thin films. The multilayer self-sensing cantilevers are designed to be thick and soft, thus combining increased deflection sensitivity with low spring constants, and hence increasing the force sensitivity. The high force sensitivity of the hybrid multilayer cantilevers is accompanied by a high detection bandwidth in AC modes. This originates from having a viscoelastic material as the main structural layer, which causes low quality factor and hence high tracking bandwidth. In terms of the imaging speed, the multilayer cantilevers show four times faster response compared to their silicon counterparts. In addition, the hermetically sealed self-sensing multilayer cantilevers can be deployed for various scanning probe microscopy (SPM) applications in liquid as well as in air and vacuum with additional coatings. For even further increase of the deflection sensitivity, newly developed high-gauge factor strain sensors can be incorporated to the multilayer cantilevers governed by their adaptable process flow. As a proof of concept, I show that atomically thin MoS2 piezoresistors can be incorporated into SU8 cantilevers. However, the MoS2 piezoresistors have very high resistance, which has an adverse effect on the force noise of the cantilevers. One common strategy to alleviate this high resistance is doping the MoS2 piezoresistors. In this work, I show that SU8 can act as a structural cantilever layer as well as an n-type doping source and an encapsulation solution for the MoS2 piezoresistors. In addition to the force resolution and the tracking ability, the quality and the repeatability of any AFM image is also correlated with the cantilever tip shape (sharpness) and durability. SU8 cantilevers have shown very good tracking ability but polymers are subjected to high wear-rate as a tip material. In the scope of my research, I have also developed fabrication recipes to integrate sharp, low wear-rate, silicon nitride tips into the pure SU8 cantilevers as well as the polymer-core multilayer cantilevers. Furthermore, to extend the ease of use and versatility of AFM, a closed-loop scanner based on a sidewall piezoresistive displacement sensor is presented. Such a closed loop scheme compensates the piezotube scanner nonlinearities, namely hysteresis and creep. This closed-loop system reshapes the piezotube drive signal through our developed FPGA-based Proportional-Integral (PI) controller.

EPFL2019

Marie Auberson, Simon Baechler, Thibault Genessay, Luc Patiny, Michaël Giuseppe Zasso

Elsevier Ireland Ltd2016

A novel microfabrication platform for hybrid multilayer MEMS

Nahid Hosseini

EPFL2019