This Master Thesis has been conducted at the National Laboratory of Forensic Science (SKL) in Linköping. When images that are to be analyzed at SKL, presenting an interesting object, are of bad quality there may be a need to enhance them. If several images with the object are available, the total amount of information can be used in order to estimate one single enhanced image. A program to do this has been developed by studying methods for image registration and high resolution image estimation. Tests of important parts of the procedure have been conducted. The final results are satisfying and the key to a good high resolution image seems to be the precision of the image registration. Improvements of this part may lead to even better results. More suggestions for further improvementshave been proposed. / Detta examensarbete har utförts på uppdrag av Statens Kriminaltekniska Laboratorium (SKL) i Linköping. Då bilder av ett intressant objekt som ska analyseras på SKL ibland är av dålig kvalitet finns det behov av att förbättra dessa. Om ett flertal bilder på objektet finns tillgängliga kan den totala informationen fråndessa användas för att skatta en enda förbättrad bild. Ett program för att göra detta har utvecklats genom studier av metoder för bildregistrering och skapande av högupplöst bild. Tester av viktiga delar i proceduren har genomförts. De slutgiltiga resultaten är goda och nyckeln till en bra högupplöst bild verkar ligga i precisionen för bildregistreringen. Genom att förbättra denna del kan troligtvis ännu bättre resultat fås. Även andra förslag till förbättringar har lagts fram.

<p>This Master Thesis has been conducted at the National Laboratory of Forensic Science (SKL) in Linköping. When images that are to be analyzed at SKL, presenting an interesting object, are of bad quality there may be a need to enhance them. If several images with the object are available, the total amount of information can be used in order to estimate one single enhanced image. A program to do this has been developed by studying methods for image registration and high resolution image estimation. Tests of important parts of the procedure have been conducted. The final results are satisfying and the key to a good high resolution image seems to be the precision of the image registration. Improvements of this part may lead to even better results. More suggestions for further improvementshave been proposed.</p> / <p>Detta examensarbete har utförts på uppdrag av Statens Kriminaltekniska Laboratorium (SKL) i Linköping. Då bilder av ett intressant objekt som ska analyseras på SKL ibland är av dålig kvalitet finns det behov av att förbättra dessa. Om ett flertal bilder på objektet finns tillgängliga kan den totala informationen fråndessa användas för att skatta en enda förbättrad bild. Ett program för att göra detta har utvecklats genom studier av metoder för bildregistrering och skapande av högupplöst bild. Tester av viktiga delar i proceduren har genomförts. De slutgiltiga resultaten är goda och nyckeln till en bra högupplöst bild verkar ligga i precisionen för bildregistreringen. Genom att förbättra denna del kan troligtvis ännu bättre resultat fås. Även andra förslag till förbättringar har lagts fram.</p>

Ground state depletion with individual molecule return (GSDIM) super-resolution microscopy is used to interrogate the location of individual fluorescence bursts from two different nanoparticle-fluorophore systems. The first system consists of fluorophore-labeled DNA molecules on gold nanowire surfaces. In this system carboxytetramethyl rhodamine-labeled double-stranded DNA molecules were bound to the surface of gold nanowires via gold-thiol linkages. The second system focuses on mesoporous silica coated nanorods with dye embedded into the silica coating. The dye molecule, Rhodamine 6G, was incorporated into the silica shell during the nanorod coating procedure. Individual fluorescence bursts were spatially localized using point spread function fitting and used to reconstruct the image of the underlying nanowire or nanorod. / text

Στην παρούσα εργασία εξετάζεται η αύξηση της ανάλυσης (super resolution) σε θερμικές εικόνες χρησιμοποιώντας τεχνικές συμπιεσμένης καταγραφής (compressed sensing). Οι εικόνες εκφράζονται με αραιό τρόπο ως προς δυο υπερπλήρη λεξικά (ένα χαμηλής και ένα υψηλής ανάλυσης) και επιχειρούμε κατασκευή της εικόνας υψηλής ανάλυσης. Τα αποτελέσματα της μεθόδου αυτής συγκρίνονται με τα αποτελέσματα τεχνικών που χρησιμοποιούν image registration με ακρίβεια subpixel για την επίτευξη του super resolution. / This thesis deals with the problem of resolution enhancement (super resolution) of thermal images using com- pressed sensing methods. We solve the super resolution problem in four stages. First, we seek a sparse representation of a low-resolution image with respect to two statistically-learned overcomplete dictionaries (for high and low resolution images respectively) and then we use the coefficients of this representa- tion to calculate the high resolution image. Then, we calculate the high resolution image using methods requiring multiple low resolution images aligned with subpixel accuracy (conventional approach). We compare the results of each method using broadly acclaimed metrics regarding reconstruction quality standards.

Medical imaging is the main tool to extract a 3D modelling of the human body or specific organs within it. In order to accomplish this, various imaging modalities have been developed over the years, such as X-Ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Each one is based on a particular energy source that passes through the body and on specific physical laws, which define the meaning of noise and the sensitivity of the imaging process. In all medical imaging systems the main goal is to increase resolution since higher resolution is a key factor in increased information content, which is critical for increased accuracy in the understanding of the anatomy and in the assessment of size and morphological structure of organs, for early detection of abnormalities, suspected pathologies and more. In order to overcome the resolution limitations, one promising idea is to use signal processing techniques to enhance the spatial resolution. This approach proposes the acquisition of a high-resolution (HR) image from observed multiple low-resolution (LR) images. This image restoration approach is called super resolution (SR) image reconstruction (or restoration). It is the process of combining multiple low resolution images to form a high resolution image. The basic requirement in order to apply SR restoration techniques is the availability of multiple LR images captured from the same scene, which are sub-sampled (aliased) as well as shifted with subpixel precision. Each observed LR image is expressed as the result of a sequence of operators on the original HR image source, consisting of a geometrical warp, blurring and down-sampling. The SR image reconstruction method consists of three stages, registration, interpolation and restoration (i.e., inverse procedure). In the registration stage, the relative shifts between LR images, with reference to a certain LR image, are estimated with fractional pixel accuracy. Accurate sub-pixel motion estimation is a very important factor in the success of the SR image reconstruction algorithm. Since the shifts between LR images are arbitrary, the registered HR image will not always match up to a uniformly spaced HR grid. Thus, non-uniform interpolation is necessary, to obtain a uniformly spaced HR image from a non-uniformly spaced composite of LR images. Finally, image restoration is applied to the up-sampled image to remove blurring and noise. In order to evaluate the performance of SR reconstruction, a ‘simulate and correct’ approach to reconstruction is selected. First, simulated images of a computer generated phantom are formed and processed in order to comply with the observation model for the LR images. These are used as the images from which the HR image is constructed through the SR method. The iterative back-projection (IBP) algorithm suggested by Irani and Peleg has been chosen to be utilized, which belongs in the spatial domain methods and it is an easily and intuitively understood method. The results of the SR reconstruction are presented separately for the axial and the transaxial case. The evaluation relies on qualitative measures of image enhancement and on objective quantitative measures, such as the resolution (FWHM), the signal-to-noise ratio, the contrast ratio and the contrast-to-noise ratio. The performed trials demonstrated improvement in both the axial and transaxial resolution. The super-resolution images also provide a significantly improved contrast ratio, which is important for improving sensitivity for detection of small details and features. The improvement in resolution can be achieved without using any hardware changes or any increase in the patient radiation procedure. An important contribution of super-resolution is also the reduction of partial volume effects in the reconstructed image. The loss in SNR, which is a typical characteristic of all resolution enhancement algorithms, was not that considerable to preclude the clinical application of super-resolution. The overall evaluation demonstrated that the SR reconstruction is a post-processing method, which can provide medical images of higher resolution and better contrast ratio, without increasing the amount of radiation or the duration of the scan. / Η ιατρική απεικόνιση είναι το κύριο εργαλείο για την τρισδιάστατη μοντελοποίηση του ανθρώπινου σώματος και συγκεκριμένων οργάνων. Για να επιτευχθεί αυτό, διάφορες μέθοδοι απεικόνισης έχουν αναπτυχθεί, όπως η Υπολογιστική Τομογραφία, η Μαγνητική Τομογραφία και η Τομογραφία Εκπομπής Ποζιτρονίου. Η κάθε μία βασίζεται σε μια συγκεκριμένη πηγή ενέργειας η οποία διαπερνά το ανθρώπινο σώμα και έχει συγκεκριμένες φυσικές ιδιότητες. Σε όλα τα συστήματα ιατρικής απεικόνισης, ο βασικός στόχος είναι η βελτίωση της διακριτικής ικανότητας και κατα συνέπεια της παρεχόμενης πληροφορίας, η οποία είναι σημαντική για την ακρίβεια στην κατανόηση της ανατομίας και στην εκτίμηση του μεγέθους και της μορφολογίας των οργάνων, για την έγκαιρη διάγνωση ανωμαλιών κλπ. Μια από τις μεθόδους που έχουν προταθεί για τη βελτίωση της διακριτικής ικανότητας είναι η χρήση τεχνικών επεξεργασίας εικόνας. Σύμφωνα με αυτή τη μέθοδο, η οποία λέγεται Super Resolution, μια εικόνα υψηλής διακριτικής ικανότητας προκύπτει από πολλαπλές εικόνες χαμηλής διακριτικής ικανότητας. Η βασική προϋπόθεση για την εφαρμογή της μεθόδου είναι η ύπαρξη πολλαπλών εικόνων χαμηλής διακριτικής ικανότητας από την ίδια σκηνή, οι οποίες είναι μετατοπισμένες με ακρίβεια ενός κλάσματος píxel. Κάθε εικόνα χαμηλής διακριτικής ικανότητας εκφράζεται σαν το αποτέλεσμα ενός γεωμετρικού μετασχηματισμού, παραμόρφωσης και υπο-δειγματοληψίας της εικόνας υψηλής διακριτικής ικανότητας. Η ανακατασκευή μιας εικόνας με τη μέθοδο Super Resolution περιλαμβάνει τρία στάδια. Στο πρώτο στάδιο, υπολογίζονται οι σχετικές μετατοπίσεις μεταξύ των εικόνων. Η ακριβής εκτίμηση αυτής της σχετικής κίνησης είναι κρίσιμος παράγοντας για την απόδοση του αλγορίθμου ανακατασκευής. Για την εκτίμηση της απόδοσης της ανακατασκευής χρησιμοποιείται ένας αλγόριθμος ‘προσομοίωσης και διόρθωσης’. Αρχικά παράγονται οι εικόνες που θα χρησιμοποιηθούν σαν βάση για την ανακατασκευή της εικόνας υψηλής διακριτικής ικανότητας. Ο αλγόριθμος που χρησιμοποιείται είναι ο IBP (iterative back-projection), όπως προτάθηκε από τους Irani, Peleg. Η εκτίμηση της απόδοσης της μεθόδου βασίζεται σε ποιοτικά και ποσοτικά κριτήρια, όπως η διακριτική ικανότητα (FWHM), το SNR και η διακριτική ικανότητα αντίθεσης. Οι δοκιμές έδειξαν βελτίωση στην διακριτική ικανότητα και στην διακριτική ικανότητα αντίθεσης, η οποία είναι σημαντική για τη βελτίωση της ικανότητας ανίχνευσης λεπτομερειών. Οι βελτιώσεις αυτές επιτυγχάνονται χωρίς αλλαγές στο επίπεδο του υλικού και χωρίς αύξηση του χρόνου έκθεσης του ασθενούς στην ακτινοβολία. Η απώλεια σε SNR, η οποία είναι τυπική συνέπεια όλων των αλγορίθμων ανακατασκευής, δεν είναι απαγορευτική για τη χρήση της μεθόδου. Η συνολική εκτίμηση της μεθόδου, δείχνει ότι είναι μια μέθοδος επεξεργασίας, μέσω της οποίας μπορούν να προκύψουν ιατρικές εικόνες υψηλής διακριτικής ικανότητας, χωρίς την αύξηση της ποσότητας της ακτινοβολίας και του χρόνου έκθεσης του ασθενούς.

No description available.

Multi Frequency Phase Measuring Profilometry is the most popular lateral contact 3-D Scanning technique. The Phase Measuring Profilometry is limited in resolution by the projector and cameras used. Conventional signal projectors have a maximum of 2000 to 4000 scan lines limiting the projector resolution. To obtain greater detail with higher resolution the PMP technique is applied to a Spatial Light Modulator (SLM) having 12000 lines, very large as compared to conventional projectors. This technology can achieve super resolution scans having varied applications. Scans achieved from PMP suffer from a certain type of artifact called “banding” which are periodic bands across the captured target. This leads to incorrect measurement of surfaces. Banding is the most limiting noise source in PMP because it increases with lower frequency and decrease in number of patterns. The requirement for lager number of patterns increases the possibility of motion banding. The requirement for higher frequency leads to the necessity for multifrequency PMP which, again leads to more patterns and longer scan times. We aim to reduce the banding by correcting the phase of the captured data.

No description available.

The resolution of the image is one of the main measures of image quality. Higher resolution is desired and often required in most of the applications, because higher resolution means more details in the image. The use of better image sensors and optics is an expensive and also limiting way of increasing pixel density within the image. The use of image processing methods, to obtain a high resolution image from low resolution images is a cheap and effective solution. This kind of image enhancement is called super resolution image reconstruction. This thesis focuses on the definition, implementation and analysis on well-known techniques of super resolution. The comparison and analysis are the main concerns to understand the improvements of the super resolution methods over single frame interpolation techniques. In addition, the comparison also gives us an insight to the practical uses of super resolution methods. As a result of the analysis, the critical examination of the techniques and their performance evaluation are achieved.

Fluorescence microscopy offers the opportunity to image biological samples noninvasively in real time and has become an essential analytical tool in the biomedical laboratory. Nonetheless, the phenomenon of diffraction imposes stringent limitations on the resolving power of conventional microscopes, preventing the spatial resolution of fluorescent species co-localized within areas of nanoscaled dimensions. Time, however, can be exploited to distinguish fluorophores within the same subdiffraction area, if their fluorescence can be switched independently, and reconstruct sequentially their spatial distribution. In this context, photolytic reactions and photochromic transformations can be invoked to switch fluorescence under optical control. Fluorescent units, such as inorganic semiconductor nanoparticles and organic dyes, and photoactive components can be operated within a common supramolecular matrix or integrated within the same molecular construct to produce photoswitchable fluorescent assemblies. In the resulting systems, electronic communication between the components can be designed in order to photoactivate or photodeactivate fluorescence respectively. Both mechanisms can be exploited to overcome diffraction, and ultimately permit the reconstruction of images with resolution down to the nanometer level, in combination with appropriate illumination protocols.