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Relevant bibliographies by topics / Image quality enhancement

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Academic literature on the topic 'Image quality enhancement'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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Journal articles on the topic "Image quality enhancement"

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Abebe, Mekides Assefa, and Jon Yngve Hardeberg. "Deep Learning Approaches for Whiteboard Image Quality Enhancement." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 360–68. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.4.040404.

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Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3457905.

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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

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Attia, Salim J. "Assessment of Some Enhancement Methods of Renal X-ray Image." NeuroQuantology 18, no. 12 (December 31, 2020): 01–05. http://dx.doi.org/10.14704/nq.2020.18.12.nq20231.

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The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

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Baqer, Ismail Sh. "Image Quality Enhancing by Efficient Histogram Equalization." Wasit Journal of Engineering Sciences 2, no. 2 (October 2, 2014): 47–58. http://dx.doi.org/10.31185/ejuow.vol2.iss2.29.

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A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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Ibrahim, Nuha Jameel, Yossra Hussain Ali, and Tarik Ahmed Rashid. "Intelligent Image Enhancement System based on Similarity Pixels." Webology 19, no. 1 (January 20, 2022): 1731–49. http://dx.doi.org/10.14704/web/v19i1/web19116.

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The main goal of image enhancement is to enhance the fine details present in the images having low luminance for better image quality. In the digital image processing field, the enhancement and removing the noise from the image is a critical issue; image noise removal is the manipulation of the image data to produce a visually high-quality image. The important details and useful information on image decreasing by the noise where the noise treated as information. The filters are used to remove unwanted information. The filters’ objectives are to improve the image quality. This paper proposes an enhancement image system, which chooses the appropriate filter and value of center pixel depends on the number of similarities adjusted neighbors pixels to the center pixel. The performance of this system is evaluated by using different quality metrics, such as Mean square error (MSE), Peak Signal Noise to Ratio (PSNR), Absolute Mean Brightness Error (AMBE), Measure of Enhancement (EME), and Measure of Enhancement by Entropy (EMEE), Entropy, Second-Order Entropy (SOE), and Image Enhancement Metric (IEM). The proposed enhancement system is efficient in removing noises and enhancing the image quality. Experiments are applied to a set of images, such as Lena, butterfly, etc. with different image sizes. The results show that the enhancement quality was performed well in the proposed system with minimal unexpected artifacts as compared to the other techniques, where the results of the proposed system for MSE, PSNR, AMBE, Entropy, SOE, EME, EMEE, and IEM for baboon image with the size 255x 255 are 2.906, 8.875, 3.92, 5.154, 2.692, 3.915, 0.442 and 3.674 in sequence.

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Gupta, Pooja, and Kuldip Pahwa. "Clock Algorithm Analysis for Increasing Quality of Digital Images." International Journal of Image and Graphics 16, no. 03 (July 2016): 1650016. http://dx.doi.org/10.1142/s0219467816500169.

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A digital image is not an exact snapshot of reality; it is only a discrete approximation. Thus, the captured images are always bit different from the images actually perceived by human eyes. These variations occur due to varying lighting conditions, weathers conditions like rain and fog, distance of scene from camera, image capturing angle, etc. The problem becomes more severe if these images are captured using low resolution image capturing devices like: Mobile phones, CCTV Cameras, Webcam, VGA cameras etc. Image enhancement addresses a solution of generating a high quality image from its low contrast version. Color enhancement is a process that differentiates objects in an image; as well as provides the detailed information of that image. This paper proposes color enhancement of low resolution digital images using clock algorithm. It is claimed that the proposed clock algorithm employed here produces good quality images in comparison with the existing color enhancement techniques. The simulation results proved that the proposed clock algorithm efficiently enhances the quality of digital low resolution images and analytically their quality improvement is observed in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and bit error rate (BER) over the existing color enhancement techniques.

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Park, So Yeon, and Byung Cheol Song. "Image Quality Enhancement for Chest X-ray images." Journal of the Institute of Electronics and Information Engineers 52, no. 10 (October 25, 2015): 97–107. http://dx.doi.org/10.5573/ieie.2015.52.10.097.

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Li, Wenxia, Chi Lin, Ting Luo, Hong Li, Haiyong Xu, and Lihong Wang. "Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration." Symmetry 14, no. 3 (March 10, 2022): 558. http://dx.doi.org/10.3390/sym14030558.

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Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.

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Wang, Qiu Yun. "Depth Estimation Based Underwater Image Enhancement." Advanced Materials Research 926-930 (May 2014): 1704–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1704.

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According to the image formation model and the nature of underwater images, we find that the effect of the haze and the color distortion seriously pollute the underwater image data, lowing the quality of the underwater images in the visibility and the quality of the data. Hence, aiming to reduce the noise and the haze effect existing in the underwater image and compensate the color distortion, the dark channel prior model is used to enhance the underwater image. We compare the dark channel prior model based image enhancement method to the contrast stretching based method for image enhancement. The experimental results proved that the dark channel prior model has good ability for processing the underwater images. The super performance of the proposed method is demonstrated as well.

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Cui, Fa Yi, and Lei Lei Ma. "Adaptive Image Generalized Fuzzy Enhancement and Quality Evaluation." Applied Mechanics and Materials 590 (June 2014): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.590.736.

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Before image feature detection and recognition, image enhancement can highlight the main people or things and their details from foreground, and also can suppress the useless information from background effectively. An algorithm model of adaptive image generalized fuzzy enhancement is established. For all aspects of the algorithm model, a variety of computing forms are put forward, and the evaluation standard of image quality is defined. The principle of algorithm is to achieve space transform between image gray space and generalized fuzzy space using generalized membership transform and its adverse transform. In the process of space transform, the contrast among successive region for space of generalized fuzzy membership grade is enhanced by generalized fuzzy enhancement function. Enhanced images are evaluated by quality standard, and the optimal values of adjustable parameters of membership grade transformation function and the fuzzy enhancement function are selected adaptively based on the optimal quality. Then, the enhanced image with best quality can be obtained. Experiments show that the extracted contour of enhanced image is structured, weak-edge-highlighting, and rich-detail.

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Dissertations / Theses on the topic "Image quality enhancement"

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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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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.

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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.

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Pitkänen, P. (Perttu). "Automatic image quality enhancement using deep neural networks." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201904101454.

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Abstract. Photo retouching can significantly improve image quality and it is considered an essential part of photography. Traditionally this task has been completed manually with special image enhancement software. However, recent research utilizing neural networks has been proven to perform better in the automated image enhancement task compared to traditional methods. During the literature review of this thesis, multiple automatic neural-network-based image enhancement methods were studied, and one of these methods was chosen for closer examination and evaluation. The chosen network design has several appealing qualities such as the ability to learn both local and global enhancements, and its simple architecture constructed for efficient computational speed. This research proposes a novel dataset generation method for automated image enhancement research, and tests its usefulness with the chosen network design. This dataset generation method simulates commonly occurring photographic errors, and the original high-quality images can be used as the target data. This dataset design allows studying fixes for individual and combined aberrations. The underlying idea of this design choice is that the network would learn to fix these aberrations while producing aesthetically pleasing and consistent results. The quantitative evaluation proved that the network can learn to counter these errors, and with greater effort, it could also learn to enhance all of these aspects simultaneously. Additionally, the network’s capability of learning local and portrait specific enhancement tasks were evaluated. The models can apply the effect successfully, but the results did not gain the same level of accuracy as with global enhancement tasks. According to the completed qualitative survey, the images enhanced by the proposed general enhancement model can successfully enhance the image quality, and it can perform better than some of the state-of-the-art image enhancement methods.Automaattinen kuvanlaadun parantaminen käyttämällä syviä neuroverkkoja. Tiivistelmä. Manuaalinen valokuvien käsittely voi parantaa kuvanlaatua huomattavasti ja sitä pidetään oleellisena osana valokuvausprosessia. Perinteisesti tätä tehtävää varten on käytetty erityisiä manuaalisesti operoitavia kuvankäsittelyohjelmia. Nykytutkimus on kuitenkin todistanut neuroverkkojen paremmuuden automaattisessa kuvanparannussovelluksissa perinteisiin menetelmiin verrattuna. Tämän diplomityön kirjallisuuskatsauksessa tutkittiin useita neuroverkkopohjaisia kuvanparannusmenetelmiä, ja yksi näistä valittiin tarkempaa tutkimusta ja arviointia varten. Valitulla verkkomallilla on useita vetoavia ominaisuuksia, kuten paikallisten sekä globaalien kuvanparannusten oppiminen ja sen yksinkertaistettu arkkitehtuuri, joka on rakennettu tehokasta suoritusnopeutta varten. Tämä tutkimus esittää uuden opetusdatan generointimenetelmän automaattisia kuvanparannusmetodeja varten, ja testaa sen soveltuvuutta käyttämällä valittua neuroverkkorakennetta. Tämä opetusdatan generointimenetelmä simuloi usein esiintyviä valokuvauksellisia virheitä, ja alkuperäisiä korkealaatuisia kuvia voi käyttää opetuksen tavoitedatana. Tämän generointitavan avulla voitiin tutkia erillisten valokuvausvirheiden, sekä näiden yhdistelmän korjausta. Tämän menetelmän tarkoitus oli opettaa verkkoa korjaamaan erilaisia virheitä sekä tuottamaan esteettisesti miellyttäviä ja yhtenäisiä tuloksia. Kvalitatiivinen arviointi todisti, että käytetty neuroverkko kykenee oppimaan erillisiä korjauksia näille virheille. Neuroverkko pystyy oppimaan myös mallin, joka korjaa kaikkia ennalta määrättyjä virheitä samanaikaisesti, mutta alhaisemmalla tarkkuudella. Lisäksi neuroverkon kyvykkyyttä oppia paikallisia muotokuvakohtaisia kuvanparannuksia arvioitiin. Koulutetut mallit pystyvät myös toteuttamaan paikallisen kuvanparannuksen onnistuneesti, mutta nämä mallit eivät yltäneet globaalien parannusten tasolle. Toteutetun kyselytutkimuksen mukaan esitetty yleisen kuvanparannuksen malli pystyy parantamaan kuvanlaatua onnistuneesti, sekä tuottaa parempia tuloksia kuin osa vertailluista kuvanparannustekniikoista.

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Headlee, Jonathan Michael. "A No-reference Image Enhancement Quality Metric and Fusion Technique." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1428755761.

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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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Hettiarachchi, Don Lahiru Nirmal Manikka. "An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.

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Hinduja, Saurabh. "Pedestrian Detection in Low Quality Moving Camera Videos." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6514.

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Pedestrian detection is one of the most researched areas in computer vision and is rapidly gaining importance with the emergence of autonomous vehicles and steering assistance technology. Much work has been done in this field, ranging from the collection of extensive datasets to benchmarking of new technologies, but all the research depends on high-quality hardware such as high-resolution cameras, Light Detection and Ranging (LIDAR) and radar. For detection in low-quality moving camera videos, we use image deblurring techniques to reconstruct image frames and use existing pedestrian detection algorithms and compare our results with the leading research done in this area.

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Cai, Hongmin. "Quality enhancement and segmentation for biomedical images." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39380130.

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Cai, Hongmin, and 蔡宏民. "Quality enhancement and segmentation for biomedical images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39380130.

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Arici, Tarik. "Single and multi-frame video quality enhancement." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29722.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Yucel Altunbasak; Committee Member: Brani Vidakovic; Committee Member: Ghassan AlRegib; Committee Member: James Hamblen; Committee Member: Russ Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Books on the topic "Image quality enhancement"

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David, Shotton, ed. Electronic light microscopy: The principles and practice of video-enhanced contrast, digital intensified fluorescence, and confocal scanning light microscopy. New York: Wiley-Liss, 1993.

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Ahmad, Anees. Development of software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1996.

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International Commission on Radiation Units and Measurements., ed. Modulation transfer function of screen-film systems. Bethesda, Md., U.S.A: The Commission, 1986.

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Ahmad, Anees. Development of software to model AXAF-I image quality: Final report, contract no. NAS8-38609. [Washington, DC: National Aeronautics and Space Administration, 1996.

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An analysis of radiographic quality: Lab manual and workbook. 3rd ed. Austin, Tex: Pro-ed, 2004.

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An analysis of radiographic quality: Lab manual and workbook. 3rd ed. Gaithersburg, Md: Aspen Publishers, 1995.

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Rainer, Gulbins, ed. Photographic multishot techniques: Super-resolution, extended depth of field, stitching, high dynamic range imaging, and other image enhancement techniques. Santa Barbara, CA: Rocky Nook, 2009.

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Manning, David J. Medical imaging 2010: Image perception, observer performance, and technology assessment : 17-18 February 2010, San Diego, California, United States. Edited by SPIE (Society), Medtronic Inc, and American Association of Physicists in Medicine. Bellingham, Wash: SPIE, 2010.

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Manning, David J., and Berkman Sahiner. Medical imaging 2009: Image perception, observer performance, and technology assessment : 11-12 February 2009, Lake Buena Vista, Florida, United States. Bellingham, Wash: SPIE, 2009.

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Fauber, Terri L. Radiographic imaging and exposure. 2nd ed. St. Louis, MO: Mosby, 2004.

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Book chapters on the topic "Image quality enhancement"

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Marino, Francescomaria, and Giuseppe Mastronardi. "Quality enhancement in image enlargement." In Image Analysis and Processing, 435–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_294.

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Wu, Chaohong, Sergey Tulyakov, and Venu Govindaraju. "Image Quality Measures for Fingerprint Image Enhancement." In Multimedia Content Representation, Classification and Security, 215–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11848035_30.

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Atiea, Mohammed A., Yousef B. Mahdy, and Abdel-Rahman Hedar. "Poor Quality Watermark Barcodes Image Enhancement." In Advances in Intelligent Systems and Computing, 913–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30111-7_88.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Quantitative Metrics for Mammographic Image Quality Assessment." In Non-Linear Filters for Mammogram Enhancement, 87–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_10.

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Liu, Wengai, Rongrong Ni, and Yao Zhao. "Reversible 3D Image Data Hiding with Quality Enhancement." In Digital Forensics and Watermarking, 446–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53465-7_33.

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Zhang, Changjiang, Juan Lu, and Jinshan Wang. "Objective Quality Assessment Measurement for Typhoon Cloud Image Enhancement." In Image Analysis and Processing – ICIAP 2009, 767–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_82.

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Komninos, Charalampos, Theodoros Pissas, Blanca Flores, Edward Bloch, Tom Vercauteren, Sébastien Ourselin, Lyndon Da Cruz, and Christos Bergeles. "Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach Using High Quality iOCT 3D Scans." In Ophthalmic Medical Image Analysis, 21–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87000-3_3.

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Bilal, Mohammed, V. C. Naveen, D. Chetan, Tajuddin Shaikh, Kavita Chachadi, and Shilpa Kamath. "Quality Enhancement of The Compressed Image Using Super Resolution." In Intelligent Computing and Communication, 353–60. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_34.

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Kim, Ki Hyun, and Yong Man Ro. "Enhancement Methods of Image Quality in Screen Mark Attack." In Digital Watermarking, 474–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24624-4_38.

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Singh, Vineeta, and Vandana Dixit Kaushik. "A Typical Hybrid Optimization-Based Image Quality Enhancement Technique." In Algorithms for Intelligent Systems, 225–33. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1657-1_18.

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Conference papers on the topic "Image quality enhancement"

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Rahman, Zia-ur, Daniel J. Jobson, Glenn A. Woodell, and Glenn D. Hines. "Image enhancement, image quality, and noise." In Optics & Photonics 2005, edited by Khan M. Iftekharuddin and Abdul A. S. Awwal. SPIE, 2005. http://dx.doi.org/10.1117/12.619460.

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Long Xu, Lin Ma, Zhuo Chen, Xianyou Zeng, and Yihua Yan. "Perceptual image quality enhancement for solar radio image." In 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2016. http://dx.doi.org/10.1109/qomex.2016.7498933.

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Dong-Ki Min and Hoon-Sang Oh. "Image quality enhancement by moving the image sensor." In 2008 IEEE Sensors. IEEE, 2008. http://dx.doi.org/10.1109/icsens.2008.4716465.

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Shaw, Rodney. "Perceived image quality basis for image enhancement techniques." In Electronic Imaging 2002, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2002. http://dx.doi.org/10.1117/12.469538.

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Kuroki, Kenro, Kenji Kurosawa, Naoki Saitoh, and Hiroyoshi Konuma. "Quality enhancement of image-intensified x-ray image." In Enabling Technologies for Law Enforcement and Security, edited by Leonid I. Rudin and Simon K. Bramble. SPIE, 1997. http://dx.doi.org/10.1117/12.267170.

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Simatupang, Joni Welman, and Heri Prasetyo. "Quality Enhancement of DDBTC Decoded Image." In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2019. http://dx.doi.org/10.1109/ispacs48206.2019.8986362.

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Mohamed, Nadir Mustafa A., Liqun Lin, Weiling Chen, and Hongan Wei. "Underwater Image Quality: Enhancement and Evaluation." In 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC). IEEE, 2020. http://dx.doi.org/10.1109/csrswtc50769.2020.9372502.

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Klima, Milos, Karel Fliegel, and Jan Svihlik. "Quality Enhancement in Security Image Information." In Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology. IEEE, 2006. http://dx.doi.org/10.1109/ccst.2006.313430.

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Goyal, Shishir, Shivansh Thapliyal, Siddharth Sharma, Souradeep Banerjee, and Jagdish Chandra Patni. "Image Quality Enhancement in C Programming." In 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019. http://dx.doi.org/10.1109/aicai.2019.8701235.

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O'Quinn, Wesley, and Rami J. Haddad. "Image Quality Enhancement Using Machine Learning." In SoutheastCon 2018. IEEE, 2018. http://dx.doi.org/10.1109/secon.2018.8479289.

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Reports on the topic "Image quality enhancement"

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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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