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Дисертації з теми "Image enhancement"
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1
Karelid, Mikael. "Image Enhancement over a Sequence of Images." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12523.
Повний текст джерелаАнотація:
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.
2
Richardson, Richard Thomas. "Image Enhancement of Cancerous Tissue in Mammography Images." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/39.
Повний текст джерелаАнотація:
This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined as temporal subtraction, dual-energy subtraction, and Digital Database for Screening Mammography (DDSM). Image enhancement begins by applying image registration and subtraction using Matlab 2012a registration for temporal images and dual-energy subtraction for dual-energy images. DDSM images require no registration or subtraction as they are used for baseline analysis. The image data are from three different sources and all images had been annotated by radiologists for each image type using an image mask to identify malignant and benign.
The second stage involved the examination of four different thresholding techniques. The amplitude thresholding method manipulates objects and backgrounds in such a way that object and background pixels have grey levels grouped into two dominant and different modes. In these cases, it was possible to extract the objects from the background using a threshold that separates the modes. The local thresholding introduced posed no restrictions on region shape or size, because it maximized edge features by thresholding local regions separately. The overall histogram analysis showed minima and maxima of the image and provided four feature types--mean, variance, skewness, and kurtosis. K-means clustering provided sequential splitting, initially performing dynamic splits. These dynamic splits were then further split into smaller, more variant regions until the regions of interest were isolated. Regional-growing methods used recursive splitting to partition the image top-down by using the average brightness of a region. Each thresholding method was applied to each of the three image types.
In the final stage, the training set and test set were derived by applying the four thresholding methods on each of the three image types. This was accomplished by running Matlab 2012a grey-level, co-occurrence matrix (GLCM) and utilizing 21 target feature types, which were obtained from the Matlab function texture features. An additional four feature types were obtained from the state of the histogram-based features types. These 25 feature types were applied to each of the two classifications malignant and benign. WEKA 3.6.10 was used along with classifier J48 and cross-validation 10 fold to find the precision, recall, and f-measure values. Best results were obtained from these two combinations: temporal subtraction with amplitude thresholding, and temporal subtraction with regional-growing thresholding. To summarize, the researcher's contribution was to assess the effectiveness of various thresholding methods in the context of a three-stage approach, to help radiologists find cancerous tissue lesions in CT and MRI mammography images.
3
Ozyurek, Serkan. "Image Dynamic Range Enhancement." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613603/index.pdf.
Повний текст джерелаАнотація:
In this thesis, image dynamic range enhancement methods are studied in order to solve the problem of representing high dynamic range scenes with low dynamic range images. For this purpose, two main image dynamic range enhancement methods, which are high dynamic range imaging and exposure fusion, are studied. More detailed analysis of exposure fusion algorithms are carried out because the whole enhancement process in the exposure fusion is performed in low dynamic
range, and they do not need any prior information about input images. In order to evaluate the performances of exposure fusion algorithms, both objective and subjective quality metrics are used. Moreover, the correlation between the
objective quality metrics and subjective ratings is studied in the experiments.
4
Chana, Deeph S. "Image restoration exploiting statistical models of the image capture process." Thesis, King's College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246886.
Повний текст джерела
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Tummala, Sai Virali, and Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.
Повний текст джерела
6
Emiroglu, Ibrahim. "Fingerprint image enhancement & recognition." Thesis, University of Hertfordshire, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363500.
Повний текст джерела
7
Roelofs, Antonius Arnoldus Jozef. "Image enhancement for low vision /." Online version, 1997. http://bibpurl.oclc.org/web/25504.
Повний текст джерела
8
Majtanovic, Cveta. "AUTOMATIC ENHANCEMENT OF IMAGE MEMORABILITY." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320352.
Повний текст джерелаАнотація:
Every picture tells a story. Images are one of the most dominant types of media, uploaded an average in billions every single day and in hundreds of billions on an annual basis. Artifacts depicting visual perception like photographs and other two-dimensional pictures are distributed through the growing number of image-sharing websites. Consequently, a thriving interest in understanding the whole image or objects depicted in it, its style or the emotions a picture might evoke, together with all the other image properties, became increasingly represented in research practice. This research focuses on the problem of automatically enhancing memorability of an image. Recent works in Computer Vision and Multimedia have shown that intrinsic image properties like memorability can be automatically inferred by exploiting powerful deep learning models. This research advances the state of the art in this area by addressing a novel and more challenging issue: “Can we transform an arbitrary input image and make it more memorable?”. To state this question properly one requires the existence of memorability measures. Methods for automatically increasing image memorability would have an impact in many application fields, such as education, gaming or advertising. To tackle the problem, we introduce an approach inspired by editing-by-applying-filters paradigm, adopted in photo editing applications like Instagram and Prisma. Users of the two apps generally have to go through the available filters before finding the desired solution which is turning the editing process into a resource- and time-consuming task. In the work conducted for the purpose of this thesis, we reverse the process: given an input image, we propose to automatically retrieve a set of “style seeds”, i.e., a set of style images which, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. As a result, we demonstrate that it is possible to automatically retrieve the best style seeds for a given image, thus, remarkably reducing the number of human attempts needed to find a good match. Furthermore, we show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state of the art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability. This approach arises from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style, also relying on deep models, a novel learning-based solution is proposed. The experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selection.Ogni immagine racconta una storia. Le immagini sono uno dei tipi di media più dominanti. Ogni giorno ne vengono caricate miliardi, per un totale di centinaia di miliardi in media ogni anno. Gli artefatti che rappresentano la percezione visiva come fotografie e altre immagini bidimensionali sono distribuiti attraverso un numero crescente di siti web di condivisione di immagini. Di conseguenza, un crescente interesse nel comprendere l'intera immagine o gli oggetti in essa raffigurati, il suo stile o le emozioni che un'immagine potrebbe evocare, insieme a tutte le altre proprietà dell'immagine, è diventato sempre più diffuso nella pratica di ricerca. Questa ricerca si concentra sul problema del miglioramento automatico della memorabilità di un'immagine. Recenti lavori in Computer Vision e Multimedia hanno dimostrato che proprietà intrinseche dell'immagine come la memorabilità possono essere dedotte automaticamente sfruttando potenti modelli di deep learning. Questa ricerca fa avanzare lo stato dell'arte in questo settore affrontando un problema nuovo e più impegnativo: "Possiamo trasformare un'immagine di input arbitraria e renderla più memorabile?". Per formulare correttamente questa domanda si richiede l'esistenza di misure di memorabilità. I metodi per aumentare automaticamente la memorabilità dell'immagine avrebbero un impatto in molti campi di applicazione, come l'istruzione, i giochi o la pubblicità. Per affrontare il problema, introduciamo un approccio ispirato al paradigma dell'editing-by-applying-filters, adottato in applicazioni di fotoritocco come Instagram e Prisma. Gli utenti delle due app devono generalmente passare in rassegna i filtri disponibili prima di trovare la soluzione desiderata e si tratta di un processo che trasforma l’editing in un'attività che richiede risorse e tempo. Nel lavoro svolto ai fini di questa tesi, invertiamo il processo: data un'immagine in ingresso, ci proponiamo di recuperare automaticamente un insieme di “style seed”, cioè un insieme di immagini di stile che, applicate all'immagine in ingresso attraverso un algoritmo di neural style transfer, fornisce il massimo aumento della memorabilità. Di conseguenza, dimostriamo che è possibile recuperare automaticamente i migliori style seed per una determinata immagine, riducendo così notevolmente il numero di tentativi umani necessari per trovare una buona corrispondenza. Inoltre, dimostriamo l'efficacia dell'approccio proposto con esperimenti sul dataset LaMem, disponibile al pubblico, eseguendo sia una valutazione quantitativa che uno studio sugli utenti. Per dimostrare la flessibilità del framework proposto, analizziamo anche l'impatto delle diverse scelte di implementazione, come l'utilizzo di diversi metodi state-of-the-art di neural style transfer. Infine, mostriamo diversi risultati qualitativi per fornire ulteriori approfondimenti sul legame tra stile dell'immagine e memorabilità. Questo approccio nasce dai recenti progressi nel campo della sintesi delle immagini e adotta una deep architecture per generare un'immagine memorabile da una data immagine di input e da uno style seed. È importante sottolineare che per selezionare automaticamente lo stile migliore, basandosi anche su modelli deep, viene proposta una nuova soluzione learning-based. La valutazione sperimentale, condotta su benchmark pubblicamente disponibili, dimostra l'efficacia dell'approccio proposto per la generazione di immagini memorabili attraverso la selezione automatica degli style seed.
9
Bengtsson, Martin, and Emil Ågren. "Image enhancement of license plates in images using Super Resolution." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121682.
Повний текст джерелаАнотація:
Bildgruppen på enheten för dokument och informationsteknik hos SKL har ett behov av att kunna förbättra bilder med extremt låg upplösning. Detta bildmaterial kan komma från diverse övervakningskameror där det intressanta objektet endast utgör en väldigt liten del, i detta fall registreringsskyltar på förbipasserande bilar. Att skapa en högupplöst bild av en registreringsskylt utav ett fåtal lågupplösta bilder är ett välkänt problem med ett flertal förslag på metoder och lösningar. I denna rapport kommer vi att undersöka vilka metoder som passar bäst vid bildförbättring av registreringsskyltar. Vi kommer även att skapa ett användargränssnitt där man kan läsa in en bild och välja mellan att automatiskt hitta registreringsskylten i bilden eller att manuellt klippa ut den. Efter att man erhållit en bild innehållandes endast registreringsskylten ska de olika implementerade bildförbättringsmetoderna kunna användas. Slutligen diskuteras vilka för- och nackdelar de respektive metoderna har. Förslag på eventuella förbättringar och hur man kan utveckla dessa metoder vidare presenteras därtill.
10
Adolfsson, Karin. "Visual Evaluation of 3D Image Enhancement." Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7944.
Повний текст джерелаАнотація:
Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.
The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.
The results of this work are;
1. a parameter setting tool that makes the initial parameter setting much easier and
2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.
11
Tan, Juan Boon. "Image enhancement in scanning optical microscopy." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306886.
Повний текст джерела
12
Temizel, Alptekin. "Wavelet domain image resolution enhancement methods." Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425928.
Повний текст джерела
13
Li, Yuanzhen Ph D. Massachusetts Institute of Technology. "Perceptually inspired image estimation and enhancement." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/49739.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.Includes bibliographical references (p. 137-144).
In this thesis, we present three image estimation and enhancement algorithms inspired by human vision. In the first part of the thesis, we propose an algorithm for mapping one image to another based on the statistics of a training set. Many vision problems can be cast as image mapping problems, such as, estimating reflectance from luminance, estimating shape from shading, separating signal and noise, etc. Such problems are typically under-constrained, and yet humans are remarkably good at solving them. Classic computational theories about the ability of the human visual system to solve such under-constrained problems attribute this feat to the use of some intuitive regularities of the world, e.g., surfaces tend to be piecewise constant. In recent years, there has been considerable interest in deriving more sophisticated statistical constraints from natural images, but because of the high-dimensional nature of images, representing and utilizing the learned models remains a challenge. Our techniques produce models that are very easy to store and to query. We show these techniques to be effective for a number of applications: removing noise from images, estimating a sharp image from a blurry one, decomposing an image into reflectance and illumination, and interpreting lightness illusions. In the second part of the thesis, we present an algorithm for compressing the dynamic range of an image while retaining important visual detail. The human visual system confronts a serious challenge with dynamic range, in that the physical world has an extremely high dynamic range, while neurons have low dynamic ranges.
(cont.) The human visual system performs dynamic range compression by applying automatic gain control, in both the retina and the visual cortex. Taking inspiration from that, we designed techniques that involve multi-scale subband transforms and smooth gain control on subband coefficients, and resemble the contrast gain control mechanism in the visual cortex. We show our techniques to be successful in producing dynamic-range-compressed images without compromising the visibility of detail or introducing artifacts. We also show that the techniques can be adapted for the related problem of "companding", in which a high dynamic range image is converted to a low dynamic range image and saved using fewer bits, and later expanded back to high dynamic range with minimal loss of visual quality. In the third part of the thesis, we propose a technique that enables a user to easily localize image and video editing by drawing a small number of rough scribbles. Image segmentation, usually treated as an unsupervised clustering problem, is extremely difficult to solve. With a minimal degree of user supervision, however, we are able to generate selection masks with good quality. Our technique learns a classifier using the user-scribbled pixels as training examples, and uses the classifier to classify the rest of the pixels into distinct classes. It then uses the classification results as per-pixel data terms, combines them with a smoothness term that respects color discontinuities, and generates better results than state-of-art algorithms for interactive segmentation.
by Yuanzhen Li.
Ph.D.
14
Yao, Peter Y. (Peter Yukon). "Image enhancement using statistical spatial segmentation." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40207.
Повний текст джерелаАнотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 56-58).
by Peter Y. Yao.
M.Eng.
15
SETHI, RAJNI. "SOME STUDIES ON UNDERWATER IMAGE ENHANCEMENT." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18451.
Повний текст джерелаАнотація:
Oceans covers 70% of the earth and are required to be explored for various scientific
studies like setting up underwater pipelines, knowing the ecological balance by taking population
census of various aquatic species etc. To get such information, capturing images is
safe and effective solution, but underwater imagery suffers from poor contrast, bluish green
color cast, hazy appearance, blurring and noise due to inherent property of water.
Enhancement of underwater images is a challenging task as images are captured in different
media having varying salinity, turbidity, green matter etc. and captured at varying depth.
Thus, there is a need of an adaptive method which works for all type of underwater images.
This thesis provides an effective solution to pre-process the underwater images to make
them suitable for scientific studies. Under the umbrella of pre-processing, sub-problems
like color correction, contrast correction , haze removal have been addressed irrespective
of the underwater media and depth at which the images have been captured. The contributions
presented in this thesis are outlined below:
• We propose an adaptive method for enhancement of underwater images by using
fuzzy rules to find the color cast. Since the color correction depends upon the media,
some parameters required for controlling the degree of color correction are found
using Multi-Objective optimization (MOPSO). Performance measures which are optimized
using MOPSO act as proper guiding mechanisms so that the resultant images
have few or no artifacts. Various non-reference based performance measures like entropy,
histogram spread, UICM have been used to analyze the results.
• We propose a local enhancement solution which tackles the artifacts caused by artificial
lighting. It performs color and contrast correction in a localized manner. The two
versions are then fused using wavelet based fusion with the help of effective weight
maps which chooses the required features from the two versions.
ii
• We proposed a complete solution which addresses the three major issues of underwater
images i.e. color cast, poor contrast, haze. Using basic image enhancement and
restoration based methods, the proposed method handles the mentioned issues for the
images captured at different depths and in media with varying salinity, turbidity.
16
Kotha, Aravind Eswar Ravi Raja, and Lakshmi Ratna Hima Rajitha Majety. "Performance Comparison of Image Enhancement Algorithms Evaluated on Poor Quality Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13880.
Повний текст джерелаАнотація:
Many applications require automatic image analysis for different quality of the input images. In many cases, the quality of acquired images is suitable for the purpose of the application. However, in some cases the quality of the acquired image has to be modified according to needs of a specific application. A higher quality of the image can be achieved by Image Enhancement (IE) algorithms. The choice of IE technique is challenging as this choice varies with the application purpose. The goal of this research is to investigate the possibility of the selective application for the IE algorithms. The values of entropy and Peak Signal to Noise Ratio (PSNR) of the acquired image are considered as parameters for selectivity. Three algorithms such as Retinex, Bilateral filter and Bilateral tone adjustment have been chosen as IE techniques for evaluation in this work. Entropy and PSNR are used for the performance evaluation of selected IE algorithms. In this study, we considered the images from three fingerprint image databases as input images to investigate the algorithms. The decision to enhance an image in these databases by the considered algorithms is based on the empirically evaluated entropy and PSNR thresholds. Automatic Fingerprint Identification System (AFIS) has been selected as the application of interest. The evaluation results show that the performance of the investigated IE algorithms affects significantly the performance of AFIS. The second conclusion is that entropy and PSNR might be considered as indicators for required IE of the input image for AFIS.
17
Azzabou, Noura. "Variable Bandwidth Image Models for Texture-Preserving Enhancement of Natural Images." Paris Est, 2008. http://pastel.paristech.org/4041/01/ThesisNouraAzzabou.pdf.
Повний текст джерелаАнотація:
Cette thèse s’intéresse aux problèmes de restauration d’images et de préservation de textures. Cette tâche nécessite un modèle image qui permet de caractériser le signal qu’on doit obtenir. Un tel model s’appuie sur la définition de l’interaction entre les pixels et qui est caractérisé par deux aspects : (1) la similarité photométrique entre les pixels (2) la distance spatiale entre les pixels qui peut être comparée à une grandeur d’échelle. La première partie de la thèse introduit un nouveau modèle non paramétrique d’image. Ce modèle permet d’obtenir une description adaptative de l’image en utilisant des noyaux de taille variable obtenue à partir d’une étape de classification effectuée au préalable. La deuxième partie introduit une autre approche pour décrire la dépendance entre pixels d’un point de vue géométrique. Ceci est effectué à l’aide d’un modèle statistique de la co-occurrence entre les observations de point de vue géométrique. La dernière partie est une nouvelle technique de sélection automatique (pour chaque pixel) de la taille des noyaux utilisé au cours du filtrage. Cette thèse est conclue avec l’application de cette dernière approche dans différents contextes de filtrage ce qui montre sa flexibilité vis-à-vis des contraintes liées aux divers problèmes traitésThis thesis is devoted to image enhancement and texture preservation issues. This task involves an image model that describes the characteristics of the recovered signal. Such a model is based on the definition of the pixels interaction that is often characterized by two aspects (i) the photometric similarity between pixels (ii) the spatial distance between them that can be compared to a given scale. The first part of the thesis, introduces novel non-parametric image models towards more appropriate and adaptive image description using variable bandwidth approximations driven from a soft classification in the image. The second part introduces alternative means to model observations dependencies from geometric point of view. This is done through statistical modeling of co-occurrence between observations and the use of multiple hypotheses testing and particle filters. The last part is devoted to novel adaptive means for spatial bandwidth selection and more efficient tools to capture photometric relationships between observations. The thesis concludes with providing other application fields of the last technique towards proving its flexibility toward various problem requirements
18
Chen, Jia. "Visual enhancement using multiple cues /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHENJ.
Повний текст джерела
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van, der Gracht Joseph. "Partially coherent image enhancement by source modification." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13379.
Повний текст джерела
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Al-Atabany, Walid Ibrahim. "Image enhancement techniques for bioelectronic visual aids." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528303.
Повний текст джерела
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Ledesma, Spencer Aguila. "A proposed framework for forensic image enhancement." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1605105.
Повний текст джерелаАнотація:
Digital images and videos used in the investigation of a crime often undergo several concurrent enhancement operations for improved analysis by humans or automated systems. When applying multiple image processing techniques to an image, the order and method in which processes are applied can have a profound impact on the result. However, the effect that one enhancement algorithm will have when applied in conjunction with another is not always obvious. When applied incorrectly, at best, there will be a negative impact to the amount of information that can be extracted from an image. At worst, the information contained in a processed image could be misrepresented. This thesis proposes a tool independent workflow for forensic image enhancement with a strong emphasis on an order of operations that maximizes the efficacy of each enhancement technique while observing the responsibilities and best practices of the forensic science community. This work will be useful for developing an understanding of common image enhancement techniques, understanding how these techniques relate to forensic science, and aiding in the creation of quality assurance standards for forensic image enhancement. Chapter 1 gives an introduction to image enhancement and discusses its role in forensic science and litigation. Chapter 2 summarizes the digital image creation process and its relationship to the human visual system. Chapter 3 reviews the most commonly used image enhancement techniques, including their theoretical background, strengths, and limitations. Chapter 4 introduces a framework for image enhancement and the rationale behind it through a series of practical examples.
22
Ström, Bartunek Josef. "FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11149.
Повний текст джерелаАнотація:
Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations. This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems. In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis.
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Albanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
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Andriolo, Stefano. "Convolutional Neural Networks in Tomographic Image Enhancement." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22843/.
Повний текст джерелаАнотація:
Convolutional Neural Networks have seen a huge rise in popularity in image applications. They have been used in medical imaging contexts to enhance the overall quality of the digital representation of the patient's scanned body region and have been very useful when dealing with limited-angle tomographic data. In this thesis, a particular type of convolutional neural network called Unet will be used as the starting point to explore the effectiveness of different networks in enhancing tomographic image reconstructions. We will first make minor tweaks to the 2-dimensional convolutional network and train it on two different datasets. After that, we will take advantage of the shape of the reconstructions we are considering to extend the convolutions to the third dimension. The scanner layout that has been considered for projecting and reconstructing volumes in this thesis indeed consits of a cone-beam geometry, whose output is a volume that approximates the original scanned object. We will then discuss the results in order to try to understand if the proposed solutions could be viable approaches for enhancing tomographic images.
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Sorensen, Matthew J. "Real-time Image Enhancement Using Texture Synthesis." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd595.pdf.
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Thomas, Bruce Allen. "New aspects of digital color image enhancement." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/289128.
Повний текст джерелаАнотація:
The spatial and chromatic dimensions of digital color image information exhibit unique interrelationships that invite new, color-image-specific, processing strategies. A quantitative exploration of these interrelationships is performed. The resulting data reveals key traits that lead to two new methods of color image enhancement. The first is a method of color image contrast enhancement that exploits the existence of high-pass spatial energy in certain chromatic color components. We present a new, spatially adaptive approach that acknowledges the spatially varying nature of cross-component correspondences. This new approach is suitable for use in any color space. The second is a method of color image denoising that exploits the unique correspondences of polychromatic multiscale edges at fine scales of analysis. The multiscale edges are derived using wavelet methods. This approach preserves image details and noticeably outperforms wavelet thresholding methods of denoising in images containing natural foliage.
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Messina, Giuseppe. "Advanced Techniques for Image Analysis and Enhancement." Thesis, Università degli Studi di Catania, 2011. http://hdl.handle.net/10761/190.
Повний текст джерелаАнотація:
The research activities, described in this thesis, have been mainly focused on images analysis and quality enhancement. Specifically the research regards the study and development of algorithms for color interpolation, contrast enhancement and red-eye removal, which have been exclusively oriented to mobile devices. Furthermore an images analysis for forgeries identification and image enhancement, usually directed by investigators (Forensic Image Processing) has been conducted. The thesis is organized in three main parts: Image Processing for Embedded Devices; Image Analysis and Enhancement; Forensics Image Processing.Le attivita' di ricerca, descritte in questa tesi, sono state principalmente focalizzate sull'analisi delle immagini ed il miglioramento della qualita'. In particolare la ricerca riguarda lo studio e lo sviluppo di algoritmi di interpolazione del colore, miglioramento del contrasto e rimozione degli occhi rossi, che sono stati esclusivamente sviluppati per l'utilizzo su dispositivi "mobile". Inoltre e' stata documentata un analisi delle immagini per l'identificazione dei falsi e per il miglioramento della qualita' immagini, a fini investigativi (Forensics Image Processing). La tesi e' organizzata in tre parti: Image Processing for Embedded Devices; Image Analysis and Enhancement; Forensics Image Processing.
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Saleem, Amina. "Image enhancement using a perceptual fusion approach." Paris 13, 2012. http://scbd-sto.univ-paris13.fr/intranet/edgalilee_th_2012_saleem.pdf.
Повний текст джерелаАнотація:
Due to limitations in image acquisition and transmission systems, image enhancement is established as an important area in image processing. Removing noise and blur, improving contrast to reveal details, coding artifact reduction and luminance adjustment are some image processing tasks that fall in the broader category of image enhancement operations. The problem of image enhancement is not a trivial task, since each image has its own characteristics and different image applications demand different enhancement requirements. It is therefore, hard to find a universal enhancement technique that would satisfy such diverse requirements. This justifies the presence of number of enhancement methods and ongoing research to find methods that can achieve different enhancement goals at the same time. Generally, image enhancement algorithms are developed to achieve some attributes of enhancement at the expense of some others. In our thesis, we develop a framework for fusion based image enhancement which is presented as a solution to the deficiencies of image enhancement algorithms. Fusion based methods for contrast enhancement, multi-focus image fusion and deblocking are developed with applications to tone mapping for HDR and natural enhancement of color imagesLa qualité de l’image perceptuelle depend essentiellement des conditions d’observation et d’acquisition, et les limitations des systems de numérisation et de transmission. On a souvent recours aux méthodes de restauration d’image et de réduction des artéfacts générés durant l’acquisition, le codage ou la transmission. Cependant, l’amélioration de la qualité d’image est un problème difficile en soi en raison de l’absence de critères objectifs bien établis pour juger des résultats. En effet, la qualité d’image est avant tout une notion subjective qui dépend de plusieurs paramètres psycho-visuels incontrôlables. De plus, chaque image a ses propres charactéristiques, et les solutions proposées dépendent aussi des applications visées. Par exemple, le réhaussement de contraste peut s’avérer efficace dans certaines zones de l’image, mais néfaste dans d’autres. Il est donc difficile de trouver une technique d’amélioration universelle qui puisse satisfaire les diverses exigences inhérentes au signal d’image. L’objectif de ce travail est de développer des méthodes basées sur une nouvelle approche où l’on fait appel à la fusion d’information et la modélisation des mécanismes de la perception visuelle. Dans ce cadre, nous proposons des méthodes de réhaussement de contraste, de filtrage et de bruit, de réductions des artefacts de codage et d’ajustement et d’équilibrage de tonalité chromatique dans le cas d’images, « HDRI ». Les performances des méthodes développées peuvent pallier les limitations des solutions de l’état de l’art et ouvrent ainsi de grandes perspectives
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江文尉. "Digital image enhancement on chromatic images." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/60366945930676014945.
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Tung, Hsiao-Jung, and 童筱蓉. "Night Image Enhancement." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/53516538666033111599.
Повний текст джерелаАнотація:
碩士輔仁大學
電機工程學系碩士班
104
Night image quality in the intelligent surveillance system is a problem. Compared to daytime images, there are always three characteristics in the night, lower brightness, lower contrast and higher noise. These features not only cause human eyes difficult to observe but also make the accuracy decreasing of the system such as object detection or tracking. Therefore, we enhance the night images by combining with contrast enhancement and denoise to solve the above three characteristics. In experiments, we simulate night image to verify that architecture, contrast enhancement and denoise are useful first. Then we applied it on the real night image and compare with other algorithms. There is a strong improvement on the indicators of brightness and contrast of the night image. As the quality, overall image brightness is higher, especially in the dark region of the image, with less and weak noise than other algorithms, which means it get a better quality images at night.
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Mallik, Sudhansu. "Underwater Image Enhancement." Thesis, 2016. http://ethesis.nitrkl.ac.in/9314/1/2016_MT_SMallik.pdf.
Повний текст джерелаАнотація:
The human incapability in diving in the deep ocean for a long time has increased the challenges of underwater analysis. Underwater remotely operated vehicles(UROVs) are used these days to acquire images in deep ocean. However, the challenges contain problems like disrupting scattering effect and aggressive light absorption in the deep ocean. Although UROVs are having artificial light, it is unable to provide good and clear images because of these problems. Three methods have been discussed in the thesis to improve the image quality. first of all, a method which contains a haze removal algorithm followed by a Contrast Limited Adaptive Histogram Equalization (CLAHE) color model has been discussed. The underwater images are enhanced through haze removal algorithm by dark channel prior technique. It shows a good result by reducing haze and noise effect still, it has a tendency to darken the image in some situation. CLAHE on RGB model has been followed in our approach to change the level of contrast and intensity of dehaze image. Secondly, an algorithm has been proposed based on Empirical Mode Decomposition (EMD) with a white balanced input to enhance the visual quality of the under-water images. The image is processed through the Gray World technique which is a white balance approach to enhance the contrast of the image and to remove the unwanted color cast in the image. Each R,G and B channel of the resultant image is decomposed into its Intrinsic Mode Functions (IMFs) by EMD process. Final enhanced image has been constructed by combining the IMFs of each channel with different optimised weights. In the end, a novel image enhancement algorithm is described for underwater images using fusion process. To improve the quality of the degraded image and to overcome from the limitations of underwater, two inputs that represent white balanced and color corrected versions of the original image have been defined. Gray World is a white balance algorithm characterized to overcome from the unwanted color casts has been implemented. To enhance the visibility of the the degraded portion, four weight maps are considered. Then each map of the resultant image has decomposed into its Intrinsic Mode Functions(IMFs) using EMD. Our experimental results show that the proposed algorithm has significantly improved quality of underwater images by enhancing the contrast of the image and reducing noise as well as artifacts in the image.
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GUPTA, KRITI. "IMAGE ENHANCEMENT USING EVOLUTIONARY COMPUTING." Thesis, 2014. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15442.
Повний текст джерелаАнотація:
A new method for the enhancement of color images is presented which uses the fuzzy logic and
differential evolution. With the application of an objective measure known as exposure the image
is divided into underexposed and overexposed regions. The V component or the luminance
component of the HSV color space is exploited for the enhancement process. However, the hue
component is kept intact so that the color constitution of the original image remains the same. The
under exposed and the over exposed regions are enhanced using two different schemes. The
enhancement of the underexposed region is done using a parametric sigmoid function whereas the
enhancement of the overexposed region is done using power law transformation. For good
enhancement results, the appropriate values of the parameters of the sigmoid function and the
gamma used in power law transformation function are required. These are optimized using
differential evolution. Moreover, two separate membership functions are used to characterize the
underexposed and the over exposed regions of the image, i.e., Gaussian membership for the
underexposed areas and triangular membership for the overexposed areas. This method becomes
universal for implementation upon all types of contrast degradations because of the use of
separate membership functions and enhancement operators for the two regions. The selfadaptation
of the parameters, based on differential evolution, makes the whole method automatic.
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CHEN, CHONG-SHU, and 陳崇樹. "Coherent image edge enhancement by image flow." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/21819831354430770598.
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Calderón, González Julian, and Salazar Òscar Daniel Carmona. "Image Enhancement with Matlab Algorithms." Thesis, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-770.
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王維綱. "Color Image Contrast Enhancement Using Image Fusion Technique." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/85252293683372085640.
Повний текст джерелаАнотація:
碩士國立交通大學
多媒體工程研究所
100
There are more and more digital images in our daily life thanks to the popularity of photograph capturing equipments, such as digital cameras and mobile phones. In addition, as the Internet and social networks have been well developed, it’s easier for people to share images with their friends. However, not all people are satisfied with the photos they taken due to the limitations of the image capturing devices. The improper luminance condition may cause under-exposed and over-exposed images. To solve this problem, plenty of researches are proposed for contrast enhancement. However, they often cannot afford to produce pleasing images for a broad variety of low contrast images or cannot be automatically applied on all images. Hence, in this thesis, we propose a classified image fusion (CIF) method for image contrast enhancement. First several virtual images having different intensities are generated. Second, the input image pixels are classified to several classes according to their luminance values. Finally, CIF was proposed to combine these exposure images to produce a fused image in which every region is well-exposed.
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Lin, Chih-Ming, and 林志明. "Image Enhancement Based on Histogram Equalizationand Image Fusion." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/64228297913611357824.
Повний текст джерелаАнотація:
碩士朝陽科技大學
資訊工程系碩士班
98
This paper presents an image enhancement approach based on histogram equalization (HE) and image fusion, which is abbreviated as IE/HEIF. The proposed IE/HEIF consists of three stages: First, obtain the complement of original image. Second, fuse the original image and its complement by linear interpolation and apply limited range histogram equalization to the fused image. Third, apply linear interpolation once more to fuse the original image and that obtained in the second stage. To improve the performance of IE/HEIF further, an adaptive scheme based on standard deviation of image blocks is employed for parameter adjustment in the linear interpolation. The approach is called IE/HEIF_std. By this doing, it is expected to have better image contrast and visual quality. To justify the proposed approaches, several examples are provided in the simulation where comparisons with other HE-based approaches are made as well. The results indicate that both IE/HEIF and IE/HEIF_std are able to enhance image contrast and visual quality and better results are generally for IE/HEIF_std as expected. Moreover, the proposed IE/HEIF and IE/HEIF_std have better enhanced images than those HE-based approaches which suffer from over-enhancement and un-natural looking in the resulted images in general.
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Li, Huei Yun, and 李蕙芸. "Image Super-resolution through Image Enhancement and Refinement." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11164033854043504526.
Повний текст джерелаАнотація:
碩士國立交通大學
多媒體工程研究所
101
Single image super-resolution has recently become a hot topic in computer vision and computer graphics communities. This technology had been applied on various media devices in our daily life. The problem how to enlarge images without artifacts and in real time is the core of super-resolution we need to solve. We propose a new method combining image enhancement and interpolation according to edge gradient information. We enhance the original input image at first. This can avoid oversmooth and let the image looked more like the ground truth image. Then we use edge direction and color information to interpolate the unknown pixels, this way can help retain the edge structures roughly. After rough image interpolation we refine the images making it accurate. This method preserves the original features and the nature texture and the whole system can be executed in adaptive time.
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Kondermann, Daniel Chen Yunqiang. "Multiple image restoration and enhancement /." 2006. http://www.gbv.de/dms/ilmenau/abs/512074879konde.txt.
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Lee, Kun-Lin, and 李昆霖. "Example-based Image Resolution Enhancement." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/35925460154273824224.
Повний текст джерелаАнотація:
碩士國立臺灣大學
資訊工程學研究所
96
When taking a photograph using digital devices such as digital cameras, usually we are not able to perfectly duplicate the scene we want to capture due to the limits of camera devices and storage space. Instead, we can only to sample the scene and store the color information of discrete space locations in the form of image pixels. The above fact arose a problem when we want to display an image on a bigger display device or zoom-in the image for checking details: There are not enough information to display an image in any resolution higher than what it was taken originally. Similarly, If we scale down the resolution of an image due to reasons like storage constraints, we will not be able to scale it back easily. The super-resolution problem is a heavily ill-posed problem, which means that a perfect solution does not exist. Which means, it is impossible to ”enlarge” an image perfectly. However, this also results in an interesting and useful research subject: How can we produce a better enlargement result with only the limited information we have? In this thesis, we assume that the after the user took a picture of the scene (target image), he/she may also took one or more pictures of that scene from a closer position (reference image), or can obtain such images from other sources (like internet photo databases etc.). In order to enlarge the target image, we first adapt a modified general examplebased algorithm to enlarge the target while trying to reduce noises often seen in results of such algorithm. Then we match the target and reference images in order to find their relative positions. Since reference images are taken closer to the scene, they include more detail information. The detail information can be used to recover the missed details at the same location in the enlarged target image. Finally, we adapt a texture transfer algorithm to synthesize details for textures in the enlarged target image similar to those on the reference images. Our result is better than traditional interpolation methods not only in the areas covered by the reference images, but also uncovered areas because of the modified general example-based method. It is also a highly flexible method since the number of reference images required is not a fixed number.
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Chin, Ya-Hsien, and 金雅祥. "Weight-Based Local Image Enhancement." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/32376124536975282066.
Повний текст джерелаАнотація:
碩士國立臺灣大學
電機工程學研究所
95
Image enhancement ranges over numerous subjects. The most common application is digital photos taken in our daily life. Due to the different environmental conditions or the photographers, it frequently leads into too dark or bright images in local area. Either in certain Pattern Recognition(PR) system, by the way of pre-processing as known as image enhancement we could obtain more distinguishing features in order to classify them further precisely. As for some indoor surveillance systems, we fail to monitor efficiently because of the poor light source generating over dark areas in a few images. Thus, we will modify this through the technology of image enhancement. Hence we wish that we could make some proper local enhancements as to meet the demands of naked-eyed observations without destroying the rest parts. Owing to the higher sensitivity of human beings to luminance and RGB color space is higher correlated not easy to process, we usually transfer images to YCbCr color space then do certain adjustments in accordance with the luminance. Through increasing the weight of low contrast area and GA optimization for solution space, we could attain the proper transfer curve for enhancing local low contrast images. Together with a use of Spatial Diffusion Filter processing the boundary problems arising from two different transfer curves, we can achieve the goal of local enhancement without destroying the quality of rest areas in the meanwhile.
41
Lee, Kun-Lin. "Example-based Image Resolution Enhancement." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-3107200818103400.
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Yang, Wei-Sheng, and 楊崴勝. "Adaptive Video Image Enhancement Filters." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74657831699903674708.
Повний текст джерелаАнотація:
碩士長榮大學
資訊管理學系碩士班
99
The purpose of this study is to improve the details of high dynamic range image through the image enhancement. The system can automatically enhance video image according to the type of image and establish the adaptive video enhancement filters. This study focuses on the image enhancement and combines with the Monte Carlo Simulation Algorithm to enhance the quality of the image. This study utilizes the advantage of the bilateral filter for high dynamic range image proessing, and combines with the advantage of the Monte Carlo Simulation Algorithm to find the proper approach of finding the optimized parameters, and make the quality of the image become higher.
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李孟儒. "Autofocus Image Contrast Enhancement for." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/18983009795712155068.
Повний текст джерелаАнотація:
碩士逢甲大學
生醫資訊暨生醫工程碩士學程
101
In this article, we propose an image enhancement technique named Autofocus Image Contrast Enhancement (AICE) for medical images. It can enhance the suspecting area and locate the Range of Interest (ROI) from medical images for diagnosis. The gray scale of tumors, hematomas and other granulation tissue are distinguished from other normal tissue. We can use the characteristic by marking the gray scalp of ROI, isolating it to help the medical diagnosis. The lesion is figured by two different ways, range growing and non-standard smoothing method are used in the algorithm to capture the lesion from images. Amplifying and increasing the difference/index of gray scale of the lesion wound detail the ROI and make the diagnosis easier by physicians. We assessed the effect of the enhanced method by Entropy, Std, ADSG, MDP, MDS. The evaluation results showed that we can increase the contrast and clear ROI of a medical image by AICE without distortion of images Key Word: Image Contrast Enhancement、Range Growing、Medical Image、Entropy、Distortion.
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KRISHNA, CHARU. "IMAGE ENHANCEMENT USING ENTROPY MAXIMIZATION." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15093.
Повний текст джерелаАнотація:
Image enhancement is one of the most interesting domain of image processing. One of the techniques of image enhancement is contrast enhancement. Contrast enhancement in image processing is a very important technique. One of the most vastly used techniques for contrast enhancement is histogram equalization. It enhances the contrast of the input image by mapping the intensity levels based on the input probability distribution function of the image. HE finds application in many fields like medical image processing. HE, in general flats the histogram of the image and thus enhances the contrast of the input image. HE the results in the stretching of dynamic range.
Although HE has low computational cost and has high performance, it is rarely used in electronic appliances as its straight use changes the original brightness of the image and hence result in distorted image. Histogram equalized image may also result in annoying artifacts and noise. Hence many techniques for contrast enhancement were developed in order to overcome the defects of HE. These include Bi-histogram brightness preserving histogram (BBHE), Dualistic sub image brightness preserving histogram(DSIH), Minimum mean brightness error bi-histogram equalization(MMBEBHE), Recursive Mean Separate Histogram Equalization(RMSHE),Entropy maximization histogram modification technique(EMHM)etc. The proposed algorithm show better results as compared to other algorithm.
45
Chang, Yu-Kuang, and 張御廣. "FUZZY IMAGE ENHANCEMENT FOR LOW ILLUMINATION IMAGE AND UNEVEN ILLUMINATION IMAGE." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/16481009654835220402.
Повний текст джерелаАнотація:
碩士大同大學
通訊工程研究所
103
In this thesis, Fuzzy image enhancement algorithms for low illumination image and uneven illumination image are proposed. The global brightness enhancement and the fuzzy contrast enhancement are performed for low illumination image. The uneven illumination enhancement and the fuzzy contrast enhancement are performed for uneven illumination image. The experimental results show that low illumination images and uneven illumination images can be effectively enhanced.
46
Pan, Lee-Chieh, and 李界磐. "On the Image Intensity Based Adaptive Image Enhancement Methods." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/46x758.
Повний текст джерелаАнотація:
碩士國立臺灣科技大學
電機工程系
95
This thesis presents two approaches of refinement image enhancements, primarily aiming to improve the brightness and low contrast images. We first propose two methods for gray images. One is Adaptive Trichotomy Histogram Equalization that can automatically adjust brightness change. The other is a design of Adaptive Trichotomy Gamma Correction that can automatically adjust brightness change by using the concept of Gamma correction in image enhancement. Both approaches can enhance very dark images. These two approaches can also be applied to color images. Specifically, we use the approaches of Adaptive Trichotomy Histogram Equalization and Adaptive Trichotomy Gamma Correction directly in color RGB model. Both approaches can reduce the possibility of color uncorrected. The approach of Adaptive Trichotomy Gamma Correction adopts look-up table to enhance images, and therefore is very efficient in performance and is ideal for real-time applications.
47
Chou, Yueh-Shia, and 周月霞. "Construct a Systematic Image Enhancement of Low Contrast Image." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/42601597441185368548.
Повний текст джерелаАнотація:
碩士元智大學
工業工程與管理學系
95
The detection technology is improved and developed continuously by electronic products. There are many researches is used image enhancement not only the detection more efficiently ,but also apply to develop new method. The method of image enhancement is decided by experience. This situation spent a lot of time to develop about the detection. The study is constructed a decision rule for image enhancement and can be get the preliminary result with our research in the shorter time. To shorten the time of chose the image enhancement, this research proposed three stage decision indexes for the electronic products. First, this stage is to separate image into space domain or frequency domain. Second, the lower control limit is used to be chosen enhancement technology in the space domain. Then, the threshold is applied to this stage that constructed enhancement technology in the frequency domain. This study is constructed enhancement routing is trained by 58 images that, and tested by 30 images. Finally, we calculated the contrast value and analyzed those data by using Boxplot and Paired-t. The contrast value of research results is significant statistically with p-value is smaller than 0.05. Thus this research can improve the contrast effectively.
48
WU, JUN-FENG, and 吳俊鋒. "Study on fuzzy image segmentation and image contrast enhancement." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/71765730092164962119.
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49
Chen, Ying-Ching, and 陳英璟. "Underwater image enhancement: Using WavelengthCompensation and Image Dehazing (WCID)." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/94271506864231404657.
Повний текст джерелаАнотація:
碩士國立中山大學
資訊工程學系研究所
99
Light scattering and color shift are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color shift corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by bluish tone. This paper proposes a novel approach to enhance underwater images by a dehazing algorithm with wavelength compensation. Once the depth map, i.e., distances between the objects and the camera, is estimated by dark channel prior, the light intensities of foreground and background are compared to determine whether an artificial light source is employed during image capturing process. After compensating the effect of artifical light, the haze phenomenon from light scattering is removed by the dehazing algorithm. Next, estimation of the image scene depth according to the residual energy ratios of different wavelengths in the background is performed. Based on the amount of attenuation corresponding to each light wavelength, color shift compensation is conducted to restore color balance. A Super-Rsolution image can offer more details that must be important and necessary in low resolution underwater image. In this paper combine Gradient-Base Super Resolution and Iterative Back-Projection (IBP) to propose Cocktail Super Resolution algorithm, with the bilateral filter to remove the chessboard effect and ringing effect along image edges, and improve the image quality. The underwater videos with diversified resolution downloaded from the Youtube website are processed by employing WCID, histogram equalization, and a traditional dehazing algorithm, respectively. Test results demonstrate that videos with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
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Liu, Ban-Yen, and 劉邦彥. "Digital Images Enhancement and Border Detection In Ventricular MRI Image." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/17521129681300012941.
Повний текст джерелаАнотація:
碩士大葉大學
工業工程研究所
87
MRI system is noninvasive and provides the clear images to diagnosis. In cardiovascular system, however, MR images require manual trace method to identify the endocardial border and the epicardial border in left ventricular. Because dynamic organs generate a huge number of images, it takes long time to identify them by using the manual trace method. To provide satisfactory clinical performance, an automatic endocardial and the epicardial border detection algorithm is required. In this research, we provide an algorithm of wavelet-based images enhancement. One hundred and sixty images from ten volunteers and divide into three groups：(1)：borders are manual tracing as a compare group, (2)：the automatic border detection algorithm is directly without images enhancement . (3)：the automatic border detection algorithm was applied after the images are enhanced by WT-based method. Finally we use the Hausdorff Distance to measure the performance of the images with or without image enhancement. Experimental results show that the endocardial profiles and the epicardial profiles can be effectively enhanced by the wavelet-based technique.