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1
Zhao, Nilu. "Haze measurements through image analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92216.
Full textAbstract:
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (page 28).
In the recent years, Singapore has been affected by haze caused by slash-and-bum fires in Indonesia. Currently, haze concentration is measured by filtering air samples at various stations in Singapore. In this thesis, optical approaches to haze measurements are explored. Images of haze were taken in fifteen minute intervals in June, 2013. These images were analyzed to obtain image contrast, and power spectral density functions. The power spectral density functions were characterized by maximum power, full width at half maximum, second and third moments, and exponential fit. Out of these methods, contrast and exponential fit results showed trend to the Pollutant Standards Index (PSI) values provided by the National Environmental Agency (NEA). Further studies on mapping contrast to PSI values are recommended.
by Nilu Zhao.
S.B.
2
Basinger, John A. "Grain Boundary Character Distribution in the HAZ of Friction Stir-Processed Al 7075 T7." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1046.pdf.
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Arigela, Sai Babu. "A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835.
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Pettersson, Niklas. "GPU-Accelerated Real-Time Surveillance De-Weathering." Thesis, Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97401.
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A fully automatic de-weathering system to increase the visibility/stability in surveillance applications during bad weather has been developed. Rain, snow and haze during daylight are handled in real-time performance with acceleration from CUDA implemented algorithms. Video from fixed cameras is processed on a PC with no need of special hardware except an NVidia GPU. The system does not use any background model and does not require any precalibration. Increase in contrast is obtained in all haze/rain/snow-cases while the system lags the maximum of one frame during rain or snow removal. De-hazing can be obtained for any distance to simplify tracking or other operating algorithms on a surveillance system.
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Francis, John W. "Pixel-by pixel reduction of atmospheric haze effects in multispectral digital imagery of water /." Online version of thesis, 1989. http://hdl.handle.net/1850/11359.
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Abbott, Joshua E. "Interactive Depth-Aware Effects for Stereo Image Editing." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3712.
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This thesis introduces methods for adding user-guided depth-aware effects to images captured with a consumer-grade stereo camera with minimal user interaction. In particular, we present methods for highlighted depth-of-field, haze, depth-of-field, and image relighting. Unlike many prior methods for adding such effects, we do not assume prior scene models or require extensive user guidance to create such models, nor do we assume multiple input images. We also do not require specialized camera rigs or other equipment such as light-field camera arrays, active lighting, etc. Instead, we use only an easily portable and affordable consumer-grade stereo camera. The depth is calculated from a stereo image pair using an extended version of PatchMatch Stereo designed to compute not only image disparities but also normals for visible surfaces. We also introduce a pipeline for rendering multiple effects in the order they would occur physically. Each can be added, removed, or adjusted in the pipeline without having to reapply subsequent effects. Individually or in combination, these effects can be used to enhance the sense of depth or structure in images and provide increased artistic control. Our interface also allows editing the stereo pair together in a fashion that preserves stereo consistency, or the effects can be applied to a single image only, thus leveraging the advantages of stereo acquisition even to produce a single photograph.
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Toro, León Paulina Fernanda. "Preferencias por imagen sialográfica adquirida con radiografía panorámica digital y con tomografía computarizada de haz cónico." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/117298.
Full textAbstract:
Trabajo de Investigación
Requisito para optar al Título de
Cirujano DentistaAutor no autoriza el acceso a texto completo de su tesis en el Portal de Tesis Electrónicas
ntroducción: La sialografía mediante Tomografía Computarizada de Haz Cónico (TCHC) ha presentado un interés creciente de la comunidad internacional de especialistas radiólogos en los últimos cinco años. En este contexto se pretendió determinar la preferencia de un grupo de especialistas en Radiología Oral y Maxilofacial entre la imagen sialográfica obtenida mediante Radiografía Panorámica Digital (RPD) y aquella obtenida mediante TCHC. Material y Método: Se realizó un estudio descriptivo de corte transversal. La muestra se compuso de diez especialistas en Radiología Oral y Maxilofacial, quienes definieron su preferencia, mediante una encuesta, por la imagen sialográfica adquirida mediante RPD o mediante TCHC en cuanto a calidad de imagen, identificación de estructuras anatómicas, y reconocimiento de patología glandular. Resultados: Las observaciones mostraron que la sialografía mediante RPD fue la opción preferida respecto a nitidez de imagen, mientras que la sialografía con TCHC fue preferida para evaluar el lóbulo profundo de la glándula parótida. Ambos exámenes fueron igualmente preferidos para visualizar el conducto excretor parotídeo y los conductillos de segundo orden, y no existió marcada preferencia entre uno u otro examen para el reconocimiento de patología glandular. Conclusión: Ambos exámenes presentan ventajas particulares a la hora de evaluar patología glandular mediante sialografía. Se sugiere investigar, en futuros estudios, las razones detrás de estas preferencias, que podrían darnos pistas de la potencialidad de uso de la Sialografía combinada con TCHC en nuestro país.
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Clark, Tad Dee. "An Analysis of Microstructure and Corrosion Resistance in Underwater Friction Stir Welded 304L Stainless Steel." Diss., BYU ScholarsArchive, 2005. http://contentdm.lib.byu.edu/ETD/image/etd872.pdf.
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Zepeda, Barrios Alejandro. "Evaluación de la Evolución Temporal en Tumores Pulmonares Tratados con Radioterapia Estereotáctica Corporal a partir de Rasgos Extraídos de las Imágenes de Tomografía Computarizada con Haz Cónico." Tesis de maestría, Universidad Autónoma del Estado de México, 2021. http://hdl.handle.net/20.500.11799/111960.
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Tesis de maestríaEn los tratamientos de radioterapia estereotáctica corporal (SBRT) es cada vez más común el uso de sistemas de tomografía computarizada por haz cónico (CBCT) montados en los aceleradores lineales, para la adquisición de imágenes tomográficas que son utilizadas para la verificación y corrección -si es el caso- del posicionamiento del paciente durante el tratamiento. En radioterapia es de importancia mayúscula asegurar que la posición del paciente sea la deseada y en SBRT esto adquiere aún mayor importancia ya que la dosis absorbida utilizada en estos procedimientos es mayor que en los casos de radioterapia de fraccionamientos convencionales (típicamente entre 10 Gy y 20 Gy por sesión de tratamiento, mientras que en otros tratamientos puede estar entre 2 Gy y 3 Gy). En el curso de SBRT, se obtiene un conjunto de imágenes de CBCT por cada sesión de tratamiento, que se compara con la tomografía de planeación para verificar que la colocación del paciente sea la indicada, regularmente las series de CBCT ya no son utilizadas para otro fin. Sin embargo, hay estudios que han demostrado que estas imágenes pueden ser utilizadas para obtener información cuantitativa de los efectos del tratamiento durante su administración, particularmente cuando se otorga en lesiones pulmonares usando SBRT. En este trabajo se buscó utilizar las imágenes de pacientes, obtenidas mediante CBCT, durante el curso de un tratamiento de SBRT de pulmón (específicamente al inicio, en una etapa intermedia y al final), para obtener rasgos cuantitativos que puedan brindar información acerca del tejido tumoral, ya sea debido a cambios en su morfología, en su intensidad de pixeles o en su textura, asociados a los efectos del tratamiento, destacando que esta evaluación se llevó a cabo solamente durante el tratamiento.. Los rasgos cuantitativos utilizados en el estudio se seleccionaron basándose en su coeficiente de variación, que nos proporciona información de su confiabilidad. A partir de las imágenes de CBCT, una vez seleccionados los rasgos, se estudió su evolución temporal del tejido tumoral a lo largo del tratamiento.
Ninguno
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Berny, Myriam. "High-temperature tests for ceramic matrix composites : from full-field regularised measurements to thermomechanical parameter identification." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST028.
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Cette thèse a pour objectif de développer une méthode de mesure de champs par corrélation d’images numériques (CIN) à haute température couplée à des mesures thermiques sur une éprouvette technologique en CMC sollicitée dans des conditions thermiques représentatives d’un environnement moteur et de mettre en place une méthodologie d’identification des propriétés thermiques et thermomécaniques du matériau, en quantifiant à chaque étape de la chaîne les incertitudes associées aux quantités d’intérêt et en les réduisant. Il a pour cela été nécessaire de traiter les défis inhérents à la CIN à chaud, que ce soit au niveau de l’acquisition des images (saturation, perte du contraste) ou de la mesure (artefacts dus à l’effet mirage, aussi appelé "brume de chaleur").Ces travaux ont ainsi donné lieu au développement d’un protocole d’étalonnage d’un banc multi-instrumenté par l’utilisation soit d’une mire in-situ, soit par auto-étalonnage en utilisant l’éprouvette elle-même et son environnement. Les mesures de déplacements 3D surfaciques (approches de stéréocorrélation globales) et les mesures thermiques ont permis de mettre en évidence ce phénomène de brume de chaleur. Des stratégies de régularisation spatiotemporelles des déplacements mesurés ont été proposées et ont permis d’obtenir des résultats satisfaisants (réduction significative des incertitudes de mesure). De même, des approches par réduction de modèles (POD) ont permis de traiter les données thermiques et de quantifier les incertitudes associées aux phénomènes convectifs. Enfin, un algorithme de recalage de modèle éléments finis pondéré sur les données de températures et de déplacements a été implémenté en vue d’identifier un ensemble de propriétés thermiques et thermomécaniques, en tenant compte de la sensibilité de chaque paramètre par rapport aux incertitudes de mesuresThe aim of this thesis is firstly to develop procedures of full-field measurements with Digital Image Correlation (DIC), coupled to thermal measurements, suitable for high-temperature experiments on CMC specimens under thermal conditions representative of an engine environment. Secondly, a methodology is proposed for identifying the thermal and thermomechanical properties of the material, quantifying at each stage of the chain the uncertainties associated with the quantities of interest and strategies to reduce them. It was necessary to deal with the challenges due to high temperatures, especially for DIC, either in terms of acquisition (saturation, loss of contrast) or measurement (artefacts due to the mirage effect, also called "heat haze effect").This work has led to the development of a calibration protocol for a multi-instrumented bench using either an in-situ calibration target or by self-calibration using the specimen itself and its environment. 3D surface displacement measurements (with global stereocorrelation approaches) and thermal measurements have made it possible to highlight the heat haze effect phenomenon. Spatiotemporal regularisation strategies of the measured displacements were proposed and allowed satisfactory results to be obtained (significant reduction of measurement uncertainties). Similarly, model reduction approaches (POD) have been used to process thermal data and quantify the uncertainties associated with convective phenomena. Finally, a weighted Finite-Element Model Updating (FEMU) algorithm on both temperature and displacement data was implemented in order to identify a set of thermal and thermomechanical properties, taking into account the sensitivity of each parameter with regard to measurement uncertainties
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Cheng, Yi-Jui, and 鄭義瑞. "Efficient Single Hazy Image Visibility Enhancement in Widely Real-World Poor Weather." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/9k66cc.
Full textAbstract:
碩士國立臺北科技大學
電腦與通訊研究所
101
The visibility of images of outdoor scenes will generally become degraded when captured during inclement weather conditions such as haze, fog, sandstorms, and so on. Additionally, localized light sources are common when capturing scenes in these conditions. Drivers often turn on the headlights of their vehicles, and streetlights are often activated. Sandstorms are particularly challenging due to the propensity of atmospheric sand to absorb specific portions of spectrum and thereby cause color-shift problems. Traditional state-of-the-art restoration techniques for hazy images are unable to effectively contend with over-saturation artifacts caused by localized light sources or color-shifts arising from inadequate spectrum absorption. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color-shifts, and thereby achieve superior restoration results for single hazy images. In order to achieve this, the proposed approach combines the hybrid dark channel prior module, the color analysis module, and the visibility recovery module. Experimental results demonstrate that the proposed haze removal technique can recover scene radiance in single images more effectively than can traditional state-of-the-art haze removal techniques.
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SINGH, PRABH PREET. "DEHAZING USING LIFTING WAVELET AND LAPLACIAN MATTE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15391.
Full textAbstract:
This work presents single image dehazing based on dark channel prior. Firstly, lifting
haar wavelet has been used to decompose the hazy image into approximation and
details. Then only the approximation component which is just one-fourth of the actual
image dimension is further processed for dehazing. The dark channel prior used in the
proposed work for dehazing is based on statistics of haze free images. The property
that the intensity of dark channel gives approximate thickness of the haze is used to
estimate the transmission and atmospheric light. Instead of constant airlight, proposed
method employs scene depth to estimate spatially varying atmospheric light as it truly
occurs in nature. Haze imaging model together with soft matting method has been
used in this work to obtain a high quality haze free image. Experimental results
demonstrate that the proposed approach produces better results as color fidelity and
contrast of haze free image are improved and no over saturation in the sky region is
observed. Further, with the use of lifting wavelet transform, reduction in
computational time by a factor of two to three as compared to the conventional
approach has been observed.
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Wang, Hui-Chih, and 王彙智. "Haze Removal in Daytime and Nighttime Scene and Simple Image Desmoking by Haze Image Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/dtz6cy.
Full textAbstract:
碩士國立臺灣大學
電信工程學研究所
105
This thesis discusses about how hazy imaging model can be applied in many fields such as daytime dehazing, nighttime dehazing or moreover, image desmoking. The first part of the thesis is about some important existing daytime dehazing algorithm such as dark channel prior method, color line method, or negative correction model. These methods take different prior information to recover the non-hazy scene. According to these different priors, we can acquire different transmission maps and recovered results. Different priors will enhance different part of hazy images depending on the original assumption of priors and lead to different performance of different dehazing algorithm. However, these algorithms are just suitable for daytime hazy images and cannot be applied on nighttime hazy images. Nighttime hazy images usually contain artificial light source and have low luminance compared with daytime hazy images. In our second part, we introduce different nighttime dehazing algorithms including color transfer method, new imaging modeling, glowing effect removal, and image fusion based method. These algorithms pre-process nighttime hazy image to make them look like daytime hazy images and use existing daytime dehazing algorithm to recover nighttime non-hazy scenes. In our third part, we propose a novel desmoking algorithm based on hazy imaging model. We discover that there is hue distortion in smoke region of smoky images which results from unbalanced particle density distributed in each color channel. In our methods, we propose single channel dehazing based on dark channel methods to make different transmission map of each color channel. Moreover, we iteratively dehaze different color channels to make residual smoke less and less. Inspired by nighttime image dehazing, we observe nighttime smoky images share the same problem with nighttime hazy images including low lighting condition and multiple scattering process between artificial light source and particles. We propose nighttime image desmoking algorithm based on our proposed daytime desmoking algorithm. Night vision enhancement preprocessing is applied to nighttime smoky image to change it to daytime-like. Proposed daytime desmoking algorithm is then applied on enhanced images. Results shows that detail in both low luminance region and smoke region is enhanced.
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Chen, Tzu-Chun, and 陳姿君. "Minmap-based Image and Video Haze Removal." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/67391462808578702266.
Full textAbstract:
碩士國立臺灣大學
資訊工程學研究所
103
A novel image and video haze removal method based on minmap is pro- posed in this paper. Minmap is defined as the channel which contains the minimal component of three RGB color channels. Because one image may contain many regions which differ in color, minmap contains different regions which show the different properties. Based on this observation, atmospheric light is estimated by region growing, dark channel prior and smooth filter we proposed separately for each minmap region. To compute veil, we estimate the whiteness based on minmap first. Then, median filter, bilateral filter and guided filter are used on whiteness to get appropriate veil. After atmospheric light and veil are computed, the hazy images can be successfully recovered using haze image model. For video version, space-time filter is proposed to ensure the temporal and spatial coherence. Compared with exiting state of the art methods, our method could have better haze removal effects. The image results demonstrate that the results are vivid and have the properly contrast in all of the regions. The video results show that the video is high quality with temporal and spatial coherence.
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Jhou, Hou-Yu, and 周厚宇. "Image Haze Removal Using Computational Intelligence Methods." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/22352544333454728309.
Full textAbstract:
碩士國立勤益科技大學
資訊工程系
102
The image quality will be reduced with the light attenuation by atmospheric particles when taking a photograph outdoors, especially in the environment with haze. Because the hazy images lose a lot of information, the image recognition systems cannot recognize the target in the image. In order to solve the problem of the image quality attenuation, there are two image dehazing methods proposed in this paper. To use the proposed fuzzy inference system is able to estimate the attenuation condition of the light. For the problem of the halo artifacts, this paper combines the morphology and the neural network to solve this problem. This paper proposes the average method to calculate the atmospheric light to solve the problem causing by the color cast. The other method is to use the fuzzy inference system to estimate the two different transmission maps which are combined by the weighted method to produce the recovered image. Finally, we demonstrate the experimental results and compare our proposed methods with other existing approaches.
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"Single image haze removal using dark channel prior." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075337.
Full textAbstract:
But haze removal is highly challenging due to its mathematical ambiguity, typically when the input is merely a single image. In this thesis, we propose a simple but effective image prior, called dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze-free images: most patches in these images should contain pixels which are dark in at least one color channel. Using this prior with a haze imaging model, we can easily recover high quality haze-free images. Experiments demonstrate that this simple prior is powerful in various situations and outperforms many previous approaches.Haze is a natural phenomenon that obscures scenes, reduces visibility, and changes colors. It is an annoying problem for photographers since it degrades image quality. It is also a threat to the reliability of many applications, like outdoor surveillance, object detection, and aerial imaging. So removing haze from images is important in computer vision/graphics.
Speed is an important issue in practice. Like many computer vision problems, the time-consuming step in haze removal is to combine pixel-wise constraints with spatial continuities. In this thesis, we propose two novel techniques to solve this problem efficiently. The first one is an unconventional large-kernel-based linear solver. The second one is a generic edge-aware filter which enables real-time performance. This filter is superior in various applications including haze removal, in terms of speed and quality.
The human visual system is able to perceive haze, but the underlying mechanism remains unknown. In this thesis, we present new illusions showing that the human visual system is possibly adopting a mechanism similar to the dark channel prior. Our discovery casts new insights into human vision research in psychology and physiology. It also reinforces the validity of the dark channel prior as a computer vision algorithm, because a good way for artificial intelligence is to mimic human brains.
He, Kaiming.
Adviser: Xiaoou Tang.
Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 131-138).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
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Fu, Yu-Hsiang, and 傅裕翔. "Single Image Based Removal of Haze and Rain." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28009491543588011264.
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碩士國立清華大學
電機工程學系
98
In general, we expect to receive vivid image, and further apply computer vision image processing on it. However, weather varies with time. Haze lowers hue saturation, degrades contrast, and affects recognition of human vision and computer vision. There exists many dehazing methods. Among them, the dark channel prior method displays impressive result, but it still cost 10~20 seconds for a 400*600 image. Therefore, we utilize it assumption and try to reduce its most time-consuming step while producing acceptable output. During the modification, we encounter the inevitable color shift problem, which is solved by recovering the optical model to its original form. In rain removal part, most current methods need video as input. Motion between frames are used to detect possible candidate rain streaks and more strict constraints are applied to further filtered out non-rain object. Blur and temporal median filter are implemented on those rain streaks. Each of these methods shows amazing results, but they fail to achieve rain removal in single image. For heavy rain, we propose a different method from the past. By viewing rain streaks as texture, it meets the image decomposition idea and MCA framework is adopted. But the dictionary is not orthogonal, thus we have to select exemplar patches from test image and train a corresponding dictionary.
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陸姿蓉. "Perspective image editing with depth from the haze." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/53141590825781131582.
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碩士國立交通大學
多媒體工程研究所
97
In this thesis, we propose a method for perspective image editing with a single landscape image. Briefly, we use haze cues to recover the transmission and depth maps in order to move the target object and still keep the scene perspective projection and reasonable occlusion in the image.
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Huang, Chih-Hsiang, and 黃致翔. "Advanced lightweight-designed convolutional neural network for image processing: single image haze removal and underwater image restoration." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/895p6m.
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碩士國立中山大學
電機工程學系研究所
107
With the flourish development of deep learning in recent years, related application is expected to become the next-generation mainstream trend. The paper aims to propose deep learning algorithm with low complexity for image processing. We select two typical ill-posed image processing problems: single image haze removal and underwater image restoration, and deal with supervised learning and unsupervised learning, respectively. In the supervised learning framework for haze removal, we reformulate the dehazing problem as restoration of the image base component and detail component. We also design the suitable network architectures in the framework to achieve high efficiency and efficacy. Besides dehazing problem, the underwater image restoration problem is also solved in this paper. To overcome the problem about the underwater images without groundtruth, the unsupervised training framework integrated two CycleGAN structures and hybrid loss function is designed to train the lightweight network for translating an underwater image into its in-air version. Experimental results show that the proposed dehazing and underwater image restoration methods achieve better performance and lower computational cost with the related state-of-the-art approaches.
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Wang, Wei-Jheng, and 王偉錚. "Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/pqcnf2.
Full textAbstract:
碩士國立臺北科技大學
電腦與通訊研究所
101
The visibility of outdoor images captured in inclement weather will become degraded due to the presence of haze, fog, sandstorms, and so on. Poor visibility caused by atmospheric phenomenon in turn causes failure in computer vision applications, such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems. In order to solve this problem, visibility restoration techniques have been developed and play an important role in many computer vision applications operating in various weather conditions. However, removing haze from a single image with a complex structure and color distortion is difficult for visibility restoration techniques to achieve. This thesis proposes a novel visibility restoration method which uses a combination of three major modules: a depth estimation module, a color analysis module, and a visibility restoration module. The proposed depth estimation module takes advantage of median filter technique and adopts our adaptive gamma correction technique. By doing so, halo effects can be avoided in images with complex structures and effective transmission map estimation can be achieved. The proposed color analysis module is based on the gray world assumption and analyzes the color characteristics of the input hazy image. Subsequently, the visibility restoration module uses the adjusted transmission map and the color-correlated information to repair the color distortion in variable scenes captured during inclement weather conditions. The experimental results demonstrate that our proposed method provides superior haze removal in comparison to the previous state-of-the-art method through visual evaluation of different scenes captured during different weather conditions.
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Tsai, Chia-Na, and 蔡佳娜. "Single Image Haze Removal Based on Transmittance of Different Light Components." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/80600276637055009836.
Full textAbstract:
碩士國立臺灣大學
資訊網路與多媒體研究所
99
Haze removal is highly desired in both computer vision applications and consumer photography. The presence of suspended particles in the atmosphere disturbs the transference of light and leads direct attenuation to a reflectance of scene radiance. When the incident light hit directly to these tiny particles, the scattered light called airlight which is covering over the reflectance of scene radiance and reaching human’s eyes or cameras forming a hazy scene or a foggy image. The haze-covered scene or images might lose details and have the color gloomy. To address this problem, an effective single image dehazing method according to the transmittance of different light components is proposed in this novel. A physics-based image degradation model of a linear combination of direct attenuation and airlight adding with optics theory are utilized in the proposed method. In this thesis, we assume that the arrived light is a type of energy which is compounded and achromatic. The color that we felt is the product of an interaction between cones in the retina and nerves of the brain. Estimating the airlight by computing a suitable value which can produce a maximum contrast. After the derivation of airlight, transmission maps are straightforward. Recovery of scene radiance is easily achieved by above steps. Experimental results of proposed method demonstrate its ability to recover the scene radiance and repair image details. The performance of proposed method is further explored via comparative studies with several state-of–the-art single image dehazing methods.
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Wang, Chi-Wei, and 王麒瑋. "Haze Removal and Sky Detection Algorithms for Photo Images." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/19210975314288564041.
Full textAbstract:
碩士國立臺灣大學
電信工程學研究所
103
Sky is one of the most significant subject matters commonly seen in outdoor photos. We propose a highly efficient sky detection algorithm. First we detect a rough sky-ground boundary. Second we calculate parameters related to the appearance of sky. Finally, we use these parameters on a probability model that indicates how possible a pixel is belong to sky. And an image processing library with parallel processing techniques is used to implement proposed algorithm. In a common desktop computer, a VGA size image only requires less than 35ms, which can be considered a threshold for real-time processing, e.g. proposed algorithm may process 480p video in real time. Another characteristic commonly presented on photo image is haze. There are a number of researches on images’ dehazing. Dehazing technique is especially useful on applications such as object recognition. However there is a tradeoff between strength of haze-removing and tones of color. If we want to remove haze as much as possible, we may sacrifice tones of color. This tradeoff usually occurs in intensity and saturation, caused by dehazing algorithms which may do some guesses about information hidden by haze. A framework has been proposed to handle the tradeoff between tones of color and strength of removing haze. We aim to make human feel the same color tones after processing. Experiment results show that our framework is efficient to remove blocky and over-saturation effects on dehazed images. Furthermore, some features are explored for a haze-degree classification system which employs SVM as the learning model. This system is suitable for image content recognition or helping adjust parameters needed by haze-removal algorithms.
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Chu, Shin-Chih, and 朱新智. "A study of MODIS Image Haze Removal and Ocean Internal Waves Detection." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/73582292235050280410.
Full textAbstract:
碩士國立成功大學
地球科學系專班
96
The technology that modern satellite image obtains advances by leaps and bounds, though has offered unprecedented abundant amount of information. However, the external environment has caused the problem of image interpretation. There are many advantages for MODIS satellite image including the global coverage, receiving twice every day, and free cost etc. It has been accepted and applied by relevant earth scientists extensively. These study results benefit long-term global land, ocean, biosphere, and atmosphere monitoring. MODIS satellite image is a passive-type optic image. Its image quality and availability is influenced usually by atmosphere cloud or haze. In order to solve this problem, this research attempts to use three methods of haze removal for MODIS satellite image including Homo-morphic filter, Wavelet transform and fusion, and Multi-scale Retinex. These haze removal methods are applied to extract useful information form damaged MODIS images. The results are compared and analyzed to determine which method is suitable for the haze removal in the study. The experimental results show that Multi-scale Retinex is slower than both of the Homo-morphic filter, and Wavelet transform and fusion. However, according to the Canny edge detection method applied to above results, the Multi-scale Retinex is better than the other two methods. In addition, these three methods can really remove haze and it is benefit to the oceanic internal waves feature detection. Finally, this research performed a Multi-scale linear feature extraction algorithm to extract the visual distinct major linear feature of oceanic internal waves. The shade lines of cloud and haze have been also removed.
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Mo, Dengkui. "Further Developing Processing Techniques of Optical Satellite Images in the Context of Forest Monitoring." Thesis, 2018. http://hdl.handle.net/11858/00-1735-0000-002E-E47B-F.
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HUANG, WEI-LIN, and 黃韋霖. "Image Haze Removal using Dark Channel Prior Technology with Adaptive Size of Mask." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ku7vww.
Full textAbstract:
碩士朝陽科技大學
資訊工程系
107
Haze images not only reduce visual visibility, but also affect the effectiveness of image processing. Therefore, image dehazing technology is a very important technology in the field of computer vision. Among them, the representative method is the dark channel prior (DCP) dehazing technology propose by He et al. Since DCP is easy to produce halo after dehazing in some cases, this paper proposes an adaptive mask size DCP dehazing method (ADCP) to improve the halo phenomenon after DCP dehazing. This method based on the inverse of the gradient of the haze image and calculates different mask sizes. In the area with a large gradient, a smaller mask size is used to solve the halo phenomenon, while in the area with a small gradient, a larger mask size is used to achieve the effect of haze removal. And through Gaussian filter and gamma function calculation to obtain better nonlinear correspondence. Finally, the ant colony optimization (ACO) algorithm is used to find the optimal parameters of the Gaussian filter and the gamma function. In addition, we also propose a new dehazing performance index and use it as a cost function for the ACO. Experimental results confirm that the method proposed in this paper can effectively improve the halo phenomenon of haze removal image in DCP, while maintaining good haze removal performance.
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Wu, Bo-Yi, and 吳柏毅. "Design of a Haze Detection Algorithm and Hardware Implementation of an Image Dehazing Method." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/5y558p.
Full textAbstract:
碩士國立雲林科技大學
電機工程系
103
This study aimed to explore how to remove haze from a single image effectively. On the hazy weather, we can't obtain the clearly and completely image of the original scene. Thus, how to effectively remove haze from images until the objects covered with haze can be recognized by human vision would be the issue for needed discussion in image processing. In the future, if the haze removal algorithm is practically applied to surveillance products as monitors, vehicle video recorders, cameras for other uses, and so on, the amounts of calculation will be taken into account besides the good image quality. Therefore, we designed a haze detection algorithm that could clearly identify if haze was in images. If the haze detection algorithm and the haze removal algorithm were simultaneously applied to the surveillance systems, the haze detection algorithm would be used to identify if haze was in images in advance. If so, the haze removal algorithm would be carried out, and then the images would be outputted after processing. If not, we could output the images directly without the haze removal algorithm. Hence, we could leave out the processing time of the haze removal algorithm for non-haze images to achieve the purpose of lowering the amounts of calculation. This study proposed the haze removal algorithm mainly divided into two steps: first, use the dark channel prior and the mean filter to measure atmospheric light; second, use dark channel prior and the patch center point to determine if an edge existed and obtain the transmission values. With the atmospheric light and transmission values, we could employ the formula to restore haze-free image. The haze detection algorithm was divided into two steps: first, use the Hue of HSV color model to analyze the images and then segment the sky in images; second, use dark channel prior to analyze histogram intensity for identifying whether there was haze in images. Based on the proposed haze removal algorithm, we also designed a comprehensive hardware structure. Furthermore, the Verilog development tool we used is Altera’s Quartus II software version 11.0 SP1. In view of hardware design, the dark channel prior techniques of the above two steps would be simultaneously scanned. That would be beneficial to decrease the time and increase the efficiency of employing the haze removal algorithm.
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MA, ZHAOHUI, and 馬昭暉. "Enhanced Performance Enormously of Image Haze Removal of Optimized Contrast Enhancement Based on GPU." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34507677917625761304.
Full textAbstract:
碩士靜宜大學
資訊工程學系
103
In the domains of computer vision and graphical computation, image haze removal has been a significant issue. By the use of haze removal process, it can significantly improve the visibility of the scene in the image. However, most of the haze removal algorithms bring high computational cost and make algorithms failed in processing huge amount of images. In this paper, we propose a parallel image haze remove algorithm, adopting optimized contrast enhancement approach, to optimize the performance based on GPU platform. The optimization from the proposed algorithm obtains performance acceleration with about 5 times as compared the original version while the haze removal effect is the same. In order to compare the effect, we will show the original hazed image and dehazed image in different environment like hill, town and city. More information of haze free images and its original hazy images are also shown in the later chapter during this paper. Our work after improvement can process a single picture in a much higher speed after optimization and make it more sufficiently fast for large-scale application which needs image haze removal in computer vision area.
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Yu-Shan, Chen, and 陳郁姍. "Single Image Haze Removal Using White-Patch Retinex Theory and Modified Dark Channel Prior." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gb286a.
Full textAbstract:
碩士國立中央大學
電機工程學系
106
In this paper, we propose a simple but effective method to remove haze from a single input image using white-patch retinex and modified dark channel prior. In this way, we can not only restore the hazy images but also overcome three problems caused by dark channel prior, which are the halo effect along depth discontinuities in recovered images, the color distortion is serious in some weather conditions and the time-consuming process. Finally, we compare our proposed method with the dark channel prior and analyze their pros and cons.
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Chen, Yan-An, and 陳彥安. "Haze/Smoke/Sand Removal and Image Enhancement Using Human Visual System Inspired Retina Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m7d5jt.
Full textAbstract:
碩士國立臺灣大學
電信工程學研究所
106
Poor visibility in bad weather is a major problem for many applications of computer vision such as outdoor object recognition, detection, tracking, intelligent vehicles and surveillance rely heavily on the quality of image scenes. However, bad weather conditions caused by suspending particles in the air, such as haze, sand, fog, dust, and smoke that have significant size and distribution in the participating medium. These conditions may significantly degrade the visibility of a scene due to the considerable presence of particles in the atmosphere that attenuation and scatter light. These particles suspending in air result in various degrees of attenuation, scattering and absorption the light in the atmosphere. This effect may significantly reduce the contrast, limit the visibility and faded the colors of the daytime scenes and nighttime scenes, resulting in a severely degraded image. It attenuates the signal of the viewed scene. Then, impacts negatively on the accuracy of many applications of computer vision. Therefore, enhancing visibility is an inevitable task. In this thesis, we introduce about how human visual system (HVS) and haze image model can be applied in many fields such as daytime/nighttime image dahazing, color constancy, low-light enhancement, daytime/nighttime image desmoking and sand removal. The first part of the thesis is to introduce the effect of human visual system and haze image model. Then, apply these models to color constancy algorithm. The second part is about some important existing daytime/nighttime dehazing algorithm based on haze image model and HVS. We observe some differences between nighttime and daytime hazy images. First, atmospheric light in nighttime hazy images suffer from non-uniform illumination and glowing effect. Second, nighttime hazy images have low illumination and some details get lost under insufficient illuminance. Third, visible lights sources with varying colors will cause an obviously color shift in the image. We propose some new daytime/nighttime dehazing models to solve these three problems and use two daytime dahazing methods to achieve low-light enhancement algorithm. In the third part, we observe that the unbalanced particle density distributed in each RGB color channel make the smoke region of smoky images suffer from hue distortion. Moreover, the smoke region is non-homogeneous which means that the concentration of the smoke is not approximate the same in the entire scene. We propose some novel daytime/nighttime smoke removal models based on haze image model to successfully address these problems. In the last part, we propose the sand removal algorithm to remove the sandstorm in the images.
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Shiu, Yi-Shiang, and 徐逸祥. "Developing a technique for the detection and removal of cloud and haze in remote sensing images." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75838186779058444511.
Full textAbstract:
碩士國立臺灣大學
地理環境資源學研究所
94
Detection and removal of cloud and haze are arduous problems in optical remote sensing imagery processing. Thick cloud and haze have the character of high reflection, so we can set the threshold to detect and remove the areas having extremely high reflection and even mosaic the images with near dates’ ones to create clear and cloudless images. Relatively, areas covered by thin cloud and haze have the spectral characteristics of both surface features and cloud and haze, thus making it difficult to separate them. This research first processed the images with relative radiometric normalization and then transformed the images from the RGB to the HIS color model. Our assumption was that the interference of thin cloud and haze, similar to mixing a color pigment with white, would increase the color intensity and decrease the saturation of an image but would not change its hue value. Guided by this assumption, we processed the multi-temporal images and isolated areas contaminated by thin cloud and haze. The results thus suggest that an automatic method based on the HIS color model is possible for detecting thin cloud and haze on satellite images.