Relevant bibliographies by topics / Image enhancement

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image enhancement'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Image enhancement"

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B., Mrs Rajeswari. "Night Time Image Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29951.

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Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to learn the mapping between the two modalities. The architecture of MIRNet incorporates convolutional layers with residual connections to effectively capture low-light image features and generate visually pleasing enhancements. We evaluate the performance of our approach on a diverse range of nighttime scenes and compare the results against existing methods. Our experiments demonstrate that MIRNet produces superior results in enhancing nighttime images, significantly improving visibility, reducing noise, and preserving image details. The proposed approach holds promise for real-world applications where high-quality nighttime imagery is essential for decision-making and visual analysis. Keywords: Night time image enhancement, MIRNet, Deep learning, Low-light imaging, Image Processing,Convolutional neural networks (CNNs),Residual connections, Supervised learning, Dataset preparation,Imagequalityimprovement,Noisereduction,Visibilityenhancement,Surveillance,Autonomousdriving,Photography.
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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (July 30, 2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.
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Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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Prateeshwaran, P., Dr N. Keerthana, and Dr S. Kevin Andrews. "Underwater Image Enhancement Techniques." International Journal of Research Publication and Reviews 5, no. 4 (April 28, 2024): 6148–55. http://dx.doi.org/10.55248/gengpi.5.0424.1129.

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Suralkar, S. R., and Seema Rajput. "Enhancement of Images Using Contrast Image Enhancement Techniques." International Journal Of Recent Advances in Engineering & Technology 08, no. 03 (March 30, 2020): 16–20. http://dx.doi.org/10.46564/ijraet.2020.v08i03.004.

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Sri Arsa, Dewa Made, Grafika Jati, Agung Santoso, Rafli Filano, Nurul Hanifah, and Muhammad Febrian Rachmadi. "COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (February 28, 2017): 50. http://dx.doi.org/10.21609/jiki.v10i1.445.

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The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image.
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Kosugi, Satoshi, and Toshihiko Yamasaki. "Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11296–303. http://dx.doi.org/10.1609/aaai.v34i07.6790.

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This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software such as Adobe® Photoshop® for the following three benefits: enhanced images have no artifacts, the same enhancement can be applied to larger images, and the enhancement is interpretable. To incorporate image editing software into a GAN, we propose a reinforcement learning framework where the generator works as the agent that selects the software's parameters and is rewarded when it fools the discriminator. Our framework can use high-quality non-differentiable filters present in image editing software, which enables image enhancement with high performance. We apply the proposed method to two unpaired image enhancement tasks: photo enhancement and face beautification. Our experimental results demonstrate that the proposed method achieves better performance, compared to the performances of the state-of-the-art methods based on unpaired learning.
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Mu, Qi, Xinyue Wang, Yanyan Wei, and Zhanli Li. "Low and non-uniform illumination color image enhancement using weighted guided image filtering." Computational Visual Media 7, no. 4 (July 23, 2021): 529–46. http://dx.doi.org/10.1007/s41095-021-0232-x.

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AbstractIn the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.
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Morath, Julianne M., Cynthia A. Bielecki, Wanda L. Carlson, and Katharine R. MarcAurele. "Image Enhancement." AORN Journal 53, no. 5 (May 1991): 1238–47. http://dx.doi.org/10.1016/s0001-2092(07)69261-8.

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Beardsley, Tim. "Image Enhancement." Scientific American 270, no. 3 (March 1994): 14–18. http://dx.doi.org/10.1038/scientificamerican0394-14.

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Dissertations / Theses on the topic "Image enhancement"

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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.

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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.

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Richardson, Richard Thomas. "Image Enhancement of Cancerous Tissue in Mammography Images." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/39.

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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.
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Ozyurek, Serkan. "Image Dynamic Range Enhancement." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613603/index.pdf.

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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.
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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.

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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.

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Emiroglu, Ibrahim. "Fingerprint image enhancement & recognition." Thesis, University of Hertfordshire, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363500.

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Roelofs, Antonius Arnoldus Jozef. "Image enhancement for low vision /." Online version, 1997. http://bibpurl.oclc.org/web/25504.

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Majtanovic, Cveta. "AUTOMATIC ENHANCEMENT OF IMAGE MEMORABILITY." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320352.

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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.
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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.

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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.
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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.

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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.

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Books on the topic "Image enhancement"

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Celebi, Emre, Michela Lecca, and Bogdan Smolka, eds. Color Image and Video Enhancement. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09363-5.

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Wilson, William J. Millimeter-wave sensor image enhancement. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Arad, Nur. Enhancement by image-dependent warping. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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J, Wilson William. Millimeter-wave sensor image enhancement. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Roelofs, T. Image enhancement for low vision. Eindhoven: Eindhoven University, 1997.

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Cheung, Kwok-Yin. Signal processing for sonar image enhancement. Norwich: University of East Anglia, 1993.

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Newell, J. C. W. Archival retrieval: Techniques for image enhancement. London: British Broadcasting Corporation Research and Development Department, 1995.

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Highnam, Ralph. Mammographic image analysis. Dordrecht: Kluwer Academic Publishers, 1999.

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K, Park Stephen, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Digital enhancement of flow field images. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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Kefayati, Sarah. Confocal and two-photon microscopy: Image enhancement. St. Catharines, Ont: Brock University, Dept. of Physics, 2008.

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Book chapters on the topic "Image enhancement"

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Bauer, Jan, Andrej Sycev, and Karlheinz Blankenbach. "Image Enhancement." In Handbook of Visual Display Technology, 781–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_198.

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Bauer, Jan, Andrej Sycev, and Karlheinz Blankenbach. "Image Enhancement." In Handbook of Visual Display Technology, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35947-7_198-1.

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Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis, 125–72. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_4.

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Vyas, Aparna, Soohwan Yu, and Joonki Paik. "Image Enhancement." In Signals and Communication Technology, 199–231. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7272-7_6.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Enhancement." In Applications of Pulse-Coupled Neural Networks, 61–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_5.

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Highnam, Ralph, and Michael Brady. "Image Enhancement." In Computational Imaging and Vision, 123–42. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4613-5_7.

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Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis, 111–46. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_4.

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Umbaugh, Scott E. "Image Enhancement." In Digital Image Processing and Analysis, 211–94. 4th ed. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003221142-6.

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Chityala, Ravishankar, and Sridevi Pudipeddi. "Image Enhancement." In Image Processing and Acquisition using Python, 95–122. Second edition. | Boca Raton : Chapman & Hall/CRC Press, 2020. | Series: Chapman & Hall/CRC the Python series: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429243370-5.

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Pianykh, Oleg S. "Image Enhancement." In Understanding Medical Informatics, 59–77. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01760-0_5.

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Conference papers on the topic "Image enhancement"

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"Image enhancement." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586836.

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Liang, Yanmei, Li Yang, and Hailun Fan. "Image enhancement for liver CT images." In International Conference on Optical Instrumentation and Technology, edited by Toru Yoshizawa, Ping Wei, and Jesse Zheng. SPIE, 2009. http://dx.doi.org/10.1117/12.837468.

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Betz, Volkmar, and Joerg-Uwe Meyer. "Image enhancement of microscopic fluorescence images." In Barcelona - DL tentative, edited by Hans-Jochen Foth, Renato Marchesini, Halina Podbielska, Michel Robert-Nicoud, and Herbert Schneckenburger. SPIE, 1996. http://dx.doi.org/10.1117/12.230021.

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Jang, Ben K., and Roger S. Gaborski. "Image enhancement for computed radiographic images." In Medical Imaging 1995, edited by Murray H. Loew. SPIE, 1995. http://dx.doi.org/10.1117/12.208699.

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Aghagolzadeh, Sabzali, and Okan K. Ersoy. "Transform image enhancement." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ml3.

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Blockwise transform image enhancement techniques are discussed. Previously, transform image enhancement has usually been based on the discrete Fourier transform (DFT) applied to the whole image. Two major drawbacks with the DFT are high complexity of implementation involving complex multiplications and additions, with intermediate results being complex numbers, and the creation of severe block effects if image enhancement is done blockwise. In addition, the quality of enhancement is not very satisfactory. In this paper, it is shown that the best transforms for transform image coding, namely, the scrambled real discrete Fourier transform, the discrete cosine transform, and the discrete cosine-III transform, are also the best for image enhancement. Three enhancement techniques discussed in detail are alpha-rooting, modified unsharp masking, and filtering based on the human visual system response (HVS). With proper modifications, it is observed that unsharp making and HVS filtering are basically equivalent. Block effects are completely removed by using an overlap-save technique in addition to the best transform. In conclusion, transform image enhancement yields highly satisfactory performance, it is biologically sound, provides parallel models for implementation, and can be performed simultaneously with transform image coding.
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Bhattacharya, Saumik, Sumana Gupta, and Venkatesh K. Subramanian. "Localized image enhancement." In 2014 Twentieth National Conference on Communications (NCC). IEEE, 2014. http://dx.doi.org/10.1109/ncc.2014.6811269.

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Chen, Kuei-Chun. "Color image enhancement." In the 30th annual Southeast regional conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/503720.503779.

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Imaino, W., L. Crawforth, and G. Sincerbox. "Optoelectronic fringe enhancement in holographic interferometry." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.wg3.

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By polarizing the object and reconstructed wave orthogonally, two interference images are created with a phase difference of 180°, which are separated with a polarizing beam splitter. Using two video cameras, the images are electronically summed and the enhanced image instantaneously displayed. This technique allows independent enhancement of the fringe brightness since the contrast can always be adjusted optimally. The enhancement is most pronounced when the difference between diffraction efficiency of the hologram and the diffuse reflectivity of the object is large and hence is appropriate for low reflectivity objects. We present experimental data on our observed fringe enhancements as well as calculations of the dependence of the fringe enhancement on object reflectivity and diffraction efficiency of the hologram.
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Binti Sabri, Nur Rafidah, and Haniza Binti Yazid. "Image Enhancement Methods For Fundus Retina Images." In 2018 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2018. http://dx.doi.org/10.1109/scored.2018.8711106.

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Shi, Zhixin, Srirangaraj Setlur, and Venu Govindaraju. "Image Enhancement for Degraded Binary Document Images." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.305.

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Reports on the topic "Image enhancement"

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Robinson, J. E. Deconvolution filters and image enhancement. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128047.

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Coffey, Mark. Image Enhancement in a Quantum Environment. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada469994.

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Moore, D. Image enhancement equipment capabilities. Status report. Office of Scientific and Technical Information (OSTI), July 1986. http://dx.doi.org/10.2172/5496387.

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Vogel, Curtis R. Computational Methods for Image Reconstruction and Enhancement. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada381745.

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Rupert, J. D., R. A. F. Grieve, J. F. Halpenny, and V. L. Sharpton. Image enhancement system for the ORCATECH graphics computer. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1987. http://dx.doi.org/10.4095/315283.

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Wilson, Gregory L., Andrew C. Lindgren, Thomas M. Fitzgerald, Pamela S. Smith, and Russell C. Hardie. Maximum a Posteriori (MAP) Estimates for Hyperspectral Image Enhancement. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada429581.

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Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Image Modeling and Enhancement via Structured Sparse Model Selection. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada513259.

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Shin, Jun Seob. Novel techniques for image quality enhancement in ultrasound imaging tomography. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1215813.

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Felton, Melvin, and Kristan Gurton. Enhancement of Target Contrast in Polarimetric Imagery Using Image Fusion. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada519582.

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Sadler, Laurel C. U.S. Army Research Laboratory Image Enhancement Test Bed User's Manual. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada587403.

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