An a-contrario approach to quasi-periodic noise removal

International audienceImages can be affected by quasi-periodic noise. This undesirable feature manifests itself by spurious repetitive patterns covering the whole image, well localized in the Fourier domain. While notch filtering permits to get rid of this phenomenon , this however requires to first detect the resulting Fourier spikes, and, in particular, to discriminate between noise spikes and spectrum patterns caused by spatially localized textures or repetitive structures. This paper proposes a statistical a-contrario detection of noise spikes in the Fourier domain. A Matlab code is also provided

A non-local dual-domain approach to cartoon and texture decomposition

International audienceThis paper addresses the problem of cartoon and texture decomposition. Microtextures being characterized by their power spectrum, we propose to extract cartoon and texture components from the information provided by the power spectrum of image patches. Thecontribution of texture to the spectrum of a patch is detected as statistically significant spectral components with respect to a nullhypothesis modeling the power spectrum of a non-textured patch. The null-hypothesis model is built upon a coarse cartoon representationobtained by a basic yet fast filtering algorithm of the literature. Hence the term ``dual domain'': the coarse decomposition is obtained in thespatial domain and is an input of the proposed spectral approach. The statistical model is also built upon the power spectrum of patches with similar textures across the image. The proposed approach therefore falls within the family of non-local methods. Experimental results are shown in various application areas, including canvas pattern removal in fine arts painting, or periodic noise removal in remote sensing imaging

Determining displacement and strain maps immune from aliasing effect with the grid method

International audienceSpatial aliasing may affect methods based on grid image processing to retrieve displacement and strain maps in experimental mechanics. Such methods aim at estimating these maps on the surface of a specimen subjected to a loading test. Aliasing, which is often not noticeable to the naked eye in the grid images, may give spurious fringes in the strain maps. This paper presents an analysis of aliasing in this context and provides the reader with simple guidelines to minimize the effect of aliasing on strain maps extracted from grid images

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository

On-belt Tomosynthesis: 3D Imaging of Baggage for Security Inspection

This thesis describes the design, testing and evaluation of `On-belt Tomosynthesis' (ObT): a cost-e ective baggage screening system based on limited angle digital x-ray tomosynthesis and close-range photogrammetry. It is designed to be retro tted to existing airport conveyor-belt systems and to overcome the limitations of current systems creating a pseudo-3D imaging system by combining x-ray and optical imaging to form digital tomograms. The ObT design and set-up consists of a con guration of two x-ray sources illuminating 12 strip detectors around a conveyor belt curve forming an 180 arc. Investigating the acquired ObT x-ray images' noise sources and distortions, improvements were demonstrated using developed image correction methods. An increase of 45% in image uniformity was shown as a result, in the postcorrection images. Simulation image reconstruction of objects with lower attenuation coe cients showed the potential of ObT to clearly distinguish between them. Reconstruction of real data showed that objects of bigger attenuation di erences (copper versus perspex, rather than air versus perspex) could be observed better. The main conclusion from the reconstruction results was that the current imaging method needed further re nements, regarding the geometry registration and the image reconstruction. The simulation results con rmed that advancing the experimental method could produce better results than the ones which can currently be achieved. For the current state of ObT, a standard deviation of 2 mm in (a) the source coordinates, and 2 in (b) the detector angles does not a ect the image reconstruction results. Therefore, a low-cost single camera coordination and tracking solution was developed to replace the previously used manual measurements. Results obtained by the developed solution showed that the necessary prerequisites for the ObT image reconstruction could be addressed. The resulting standard deviation was of an average of 0.4 mm and 1 degree for (a) and (b) respectively