Relevant bibliographies by topics / High Dynamic Range images (HDR)

Academic literature on the topic 'High Dynamic Range images (HDR)'

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Published: 11 March 2023

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Journal articles on the topic "High Dynamic Range images (HDR)"

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Lu, Jiajun, Fangyan Dong, and Kaoru Hirota. "Gradient-Related Non-Photorealistic Rendering for High Dynamic Range Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 628–36. http://dx.doi.org/10.20965/jaciii.2013.p0628.

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A non-photorealistic rendering (NPR) method based on elements, usually strokes, is proposed for rendering high dynamic range (HDR) images to mimic the visual perception of human artists and designers. It enables strokes generated in the rendering process to be placed accurately on account of improvements in computing gradient values especially in regions having particularly high or low luminance. Experimental results using a designed pattern show that angles of gradient values obtained from HDR images have a reduction in averaged error of up to 57.5% in comparison to that of conventional digital images. A partial experiment on incorporating HDR images into other NPR styles, such as dithering, shows the wide compatibility of HDR images in providing source information for NPR processes.
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Dev, Soumyabrata, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler. "High-dynamic-range imaging for cloud segmentation." Atmospheric Measurement Techniques 11, no. 4 (April 11, 2018): 2041–49. http://dx.doi.org/10.5194/amt-11-2041-2018.

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Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.
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Choi, Sungil, Jaehoon Cho, Wonil Song, Jihwan Choe, Jisung Yoo, and Kwanghoon Sohn. "Pyramid Inter-Attention for High Dynamic Range Imaging." Sensors 20, no. 18 (September 7, 2020): 5102. http://dx.doi.org/10.3390/s20185102.

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This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
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Ledda, Patrick. "Product Review: High Dynamic Range Displays." Presence: Teleoperators and Virtual Environments 16, no. 1 (February 1, 2007): 119–22. http://dx.doi.org/10.1162/pres.16.1.119.

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In the natural world, the human eye is confronted with a wide range of colors and luminances. A surface lit by moonlight might have a luminance level of around 10−3 cd/m2, while surfaces lit during a sunny day could reach values larger than 105 cd/m2. A good quality CRT (cathode ray tube) or LCD (liquid crystal display) monitor is only able to achieve a maximum luminance of around 200 to 300 cd/m2 and a contrast ratio of not more than two orders of magnitude. In this context the contrast ratio or dynamic range is defined as the ratio of the highest to the lowest luminance. We call high dynamic range (HDR) images, those images (or scenes) in which the contrast ratio is larger than what a display can reproduce. In practice, any scene that contains some sort of light source and shadows is HDR. The main problem with HDR images is that they cannot be displayed, therefore although methods to create them do exist (by taking multiple photographs at different exposure times or using computer graphics 3D software for example) it is not possible to see both bright and dark areas simultaneously. (See Figure 1.) There is data that suggests that our eyes can see detail at any given adaptation level within a contrast of 10,000:1 between the brightest and darkest regions of a scene. Therefore an ideal display should be able to reproduce this range. In this review, we present two high dynamic range displays developed by Brightside Technologies (formerly Sunnybrook Technologies) which are capable, for the first time, of linearly displaying high contrast images. These displays are of great use for both researchers in the vision/graphics/VR/medical fields as well as professionals in the VFX/gaming/architectural industry.
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Shaya, Omar, Pengpeng Yang, Rongrong Ni, Yao Zhao, and Alessandro Piva. "A New Dataset for Source Identification of High Dynamic Range Images." Sensors 18, no. 11 (November 6, 2018): 3801. http://dx.doi.org/10.3390/s18113801.

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Digital source identification is one of the most important problems in the field of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known reference pattern noise-based source identification algorithm was tested on both kinds of images. Results have shown difficulties in source identification conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identification, thus leaving space for more research in this field.
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Ahirwal, Ramratan, Yogesh Singh Rajput, and Dr Yogendra Kumar Jain. "Ghost-Free High Dynamic Range Imaging Using Histogram Separation and Edge Preserving Denoising." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 3 (January 7, 2014): 3329–37. http://dx.doi.org/10.24297/ijct.v12i3.3242.

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In this paper, we introduce a ghost-free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamicrange (HDR) images. The multiple image fusion based HDR method work only on condition that there is no movement ofcamera and object when capturing multiple, differently exposed low dynamic range (LDR) images. The proposed algorithmmakes three LDR images from a single input image to remove such an unrealistic condition. For this purpose a histogramseparation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram.An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in thestretching process. In the proposed algorithm final HDR image free from ghost artifacts in dynamic environment because itself-generates three LDR images from a single input image. Therefore, the proposed algorithm can be use in mobilephone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuiltor post-processing software application.
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Li, Jie, Hai Wen Wang, and Xi Xi He. "Creation Method of High Dynamic Range Image Based on Three-Color Camera." Applied Mechanics and Materials 731 (January 2015): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.731.193.

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The current HDR (High-Dynamic Range) images gets expensive display devices with low dynamic range of equipment problems, research objectives are presented methods for using ordinary camera fetching and displaying high dynamic range images. General three-color camera’s use is to obtain 3 different exposures of the same scene images, and binary image pyramid, followed by low-level image panning and rotation registration step by step, using HDR Darkroom Photomatix software obtains high dynamic range images ,tone mapping and detail enhancement, using Photoshop software to fine-tune to get the final high-dynamic range images. Visual evaluation and instrumental measurements shows the synthesis of high dynamic range images can increase reflects the brightness of the scene, details and colour information, application and promotion of the value of the method.
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Gracheva, I. A., and A. V. Kopylov. "TONE COMPRESSION ALGORITHM FOR HIGH DYNAMIC RANGE MEDICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 87–95. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-87-2019.

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<p><strong>Abstract.</strong> We propose here an HDR compression method for medical images based on a windowing operator, an adaptive tone mapping operator, and the probabilistic normal-gamma model. First, we use the windowing operator based on a structural fidelity measure for optimal visualization of the input HDR medical image. Then, we transform the windowed image to the logarithm domain and split it into base and detail layers with the help of the probabilistic normal-gamma model. Base and detail layers are used to make the tone map with help the adaptive tone mapping operator. Finally, the tone mapping result is the LDR image. The proposed method has comparable quality and low computation time compared to other tone mapping operators.</p>
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See, Zi Siang, Lizbeth Goodman, Craig Hight, Mohd Shahrizal Sunar, Arindam Dey, Yen Kaow Ng, and Mark Billinghurst. "Creating high fidelity 360° virtual reality with high dynamic range spherical panorama images." Virtual Creativity 9, no. 1 (December 1, 2019): 73–109. http://dx.doi.org/10.1386/vcr_00006_1.

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Abstract This research explores the development of a novel method and apparatus for creating spherical panoramas enhanced with high dynamic range (HDR) for high fidelity Virtual Reality 360 degree (VR360) user experiences. A VR360 interactive panorama presentation using spherical panoramas can provide virtual interactivity and wider viewing coverage; with three degrees of freedom, users can look around in multiple directions within the VR360 experiences, gaining the sense of being in control of their own engagement. This degree of freedom is facilitated by the use of mobile displays or head-mount-devices. However, in terms of image reproduction, the exposure range can be a major difficulty in reproducing a high contrast real-world scene. Imaging variables caused by difficulties and obstacles can occur during the production process of spherical panorama facilitated with HDR. This may result in inaccurate image reproduction for location-based subjects, which will in turn result in a poor VR360 user experience. In this article we describe a HDR spherical panorama reproduction approach (workflow and best practice) which can shorten the production processes, and reduce imaging variables, and technical obstacles and issues to a minimum. This leads to improved photographic image reproduction with fewer visual abnormalities for VR360 experiences, which can be adaptable into a wide range of interactive design applications. We describe the process in detail and also report on a user study that shows the proposed approach creates images which viewers prefer, on the whole, to those created using more complicated HDR methods, or to those created without the use of HDR at all.
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Xie, Shundao, Wenfang Wu, Rongjun Chen, and Hong-Zhou Tan. "Reduced-Dimensional Capture of High-Dynamic Range Images with Compressive Sensing." Discrete Dynamics in Nature and Society 2020 (April 27, 2020): 1–13. http://dx.doi.org/10.1155/2020/6703528.

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The range of light illumination in real scenes is very large, and ordinary cameras can only record a small part of this range, which is far lower than the range of human eyes’ perception of light. High-dynamic range (HDR) imaging technology that has appeared in recent years can record a wider range of illumination than the perceptual range of the human eye. However, the current mainstream HDR imaging technology is to capture multiple low-dynamic range (LDR) images of the same scene with different exposures and then merge them into one HDR image, which greatly increases the amount of data captured. The advent of single-pixel cameras (compressive imaging system) has proved the feasibility of obtaining and restoring image data based on compressive sensing. Therefore, this paper proposes a method for reduced-dimensional capture of high dynamic range images with compressive sensing, which includes algorithms for front end (capturing) and back end (processing). At the front end, the K-SVD dictionary is used to compressive sensing the input multiple-exposure image sequence, thereby reducing the amount of data transmitted to the back end. At the back end, the Orthogonal Matching Pursuit (OMP) algorithm is used to reconstruct the input multiple-exposure image sequence. A low-rank PatchMatch algorithm is proposed to merge the reconstructed image sequence to obtain an HDR image. Simulation results show that, under the premise of reducing the complexity of the front-end equipment and the amount of communication data between the front end and the back end, the overall system achieves a good balance between the amount of calculation and the quality of the HDR image obtained.
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Dissertations / Theses on the topic "High Dynamic Range images (HDR)"

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Clark, Brian Sean. "Time lapse HDR: time lapse photography with high dynamic range images." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2408.

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In this thesis, I present an approach to a pipeline for time lapse photography using conventional digital images converted to HDR (High Dynamic Range) images (rather than conventional digital or film exposures). Using this method, it is possible to capture a greater level of detail and a different look than one would get from a conventional time lapse image sequence. With HDR images properly tone-mapped for display on standard devices, information in shadows and hot spots is not lost, and certain details are enhanced.
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Ramírez, Orozco Raissel. "High dynamic range content acquisition from multiple exposures." Doctoral thesis, Universitat de Girona, 2016. http://hdl.handle.net/10803/371162.

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The limited dynamic range of digital images can be extended by composing different exposures of the same scene to produce HDR images. This thesis is composed of an overview of the state of the art techniques and three methods to tackle the image alignment and deghosting problems in the HDR imaging domain. The first method detects the areas affected by motion, registers the dynamic objects over a reference image, and combines low-dynamic range values to recover HDR values in the whole image. The second approach builds multiscopic HDR images from LDR multi-exposure images. It is based on a patch match algorithm which was adapted and improved to take advantage of epipolar geometry constraints of stereo images. The last method proposes to replace under/over exposed pixels in the reference image by using valid HDR values from other images in the multi-exposure LDR image sequence.
El limitado rango dinámico de las imágenes digitales puede ampliarse mezclando varias imágenes adquiridas con diferentes valores de exposición. Esta tesis incluye un detallado resumen del estado del arte y tres métodos diferentes para alinear las imágenes y corregir el efecto ’ghosting’ en imágenes HDR. El primer método está centrado en detectar las áreas afectadas por el movimiento y registrar los objetos dinámicos sobre una imagen de referencia de modo que se logre recuperar información a lo largo de toda la imagen. Nuestra segunda propuesta es un método para obtener imágenes HDR multiscópicas a partir de diferentes exposiciones LDR. Está basado en un algoritmo de ’patch match’ que ha sido adaptado para aprovechar las ventajas de las restricciones de la geometría epipolar de imágenes estéreo. Por último proponemos reemplazar los píxeles saturados en la imagen de referencia usando valores correctos de otras imágenes de la secuencia.
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Griffiths, David John. "Developmemt of High Speed High Dynamic Range Videography." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74990.

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High speed video has been a significant tool for unraveling the quantitative and qualitative assessment of phenomena that is too fast to readily observe. It was first used in 1852 by William Henry Fox Talbot to settle a dispute with reference to the synchronous position of a horse's hooves while galloping. Since that time private industry, government, and enthusiasts have been measuring dynamic scenarios with high speed video. One challenge that faces the high speed video community is the dynamic range of the sensors. The dynamic range of the sensor is constrained to the bit depth of the analog to digital converter, the deep well capacity of the sensor site, and baseline noise. A typical high speed camera can span a 60 dB dynamic range, 1000:1, natively. More recently the dynamic range has been extended to about 80 dB utilizing different pixel acquisition methods. In this dissertation a method to extend the dynamic range will be presented and demonstrated to extend the dynamic range of a high speed camera system to over 170 dB, about 31,000,000:1. The proposed formation methodology is adaptable to any camera combination, and almost any needed dynamic range. The dramatic increase in the dynamic range is made possible through an adaptation of the current high dynamic range image formation methodologies. Due to the high cost of a high speed camera, a minimum number of cameras are desired to form a high dynamic range high speed video system. With a reduced number of cameras spanning a significant range, the errors on the formation process compound significantly relative to a normal high dynamic range image. The increase in uncertainty is created from the lack of relevant correlated information for final image formation, necessitating the development of a new formation methodology. In the proceeding text the problem statement and background information will be reviewed in depth. The development of a new weighting function, stochastic image formation process, tone map methodology, and optimized multi camera design will be presented. The proposed methodologies' effectiveness will be compared to current methods throughout the text and a final demonstration will be presented.
Ph. D.
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Zhou, Fanping. "Omnidirectional High Dynamic Range Imaging with a Moving Camera." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31324.

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Common cameras with a dynamic range of two orders cannot reproduce typical outdoor scenes with a radiance range of over five orders. Most high dynamic range (HDR) imaging techniques reconstruct the whole dynamic range from exposure bracketed low dynamic range (LDR) images. But the camera must be kept steady with no or small motion, which is not practical in many cases. Thus, we develop a more efficient framework for omnidirectional HDR imaging with a moving camera. The proposed framework is composed of three major stages: geometric calibration and rotational alignment, multi-view stereo correspondence and HDR composition. First, camera poses are determined and omnidirectional images are rotationally aligned. Second, the aligned images are fed into a spherical vision toolkit to find disparity maps. Third, enhanced disparity maps are used to warp differently exposed neighboring images to a target view and an HDR radiance map is obtained by fusing the registered images in radiance. We develop disparity-based forward and backward image warping algorithms for spherical stereo vision and implement them in GPU. We also explore some techniques for disparity map enhancement including a superpixel technique and a color model for outdoor scenes. We examine different factors such as exposure increment step size, sequence ordering, and the baseline between views. We demonstrate the success with indoor and outdoor scenes and compare our results with two state-of-the-art HDR imaging methods. The proposed HDR framework allows us to capture HDR radiance maps, disparity maps and an omnidirectional field of view, which has many applications such as HDR view synthesis and virtual navigation.
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Vančura, Jan. "Tone-mapping HDR obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237159.

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This thesis concerns with the introduction to the problematics of images with high dynamic range (HDR) and possibilities of HDR images compression options for display on devices with a low dynamic range (LDR). In the introduction is described historical evolution of recording of reality. It is focusing towards point of view of physics, human visual perception and digital recording. There are described the ways of generating and holding of HDR images. The thesis is corncerned to the techniques of HDR compression, it means the tone-mapping. The different techniques of tone-mapping are explained and specific aproach is targeted to the gradient domain high dynamic range compresion.
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Guarnieri, Gabriele. "High dynamic range images: processing, display and perceptual quality assessment." Doctoral thesis, Università degli studi di Trieste, 2009. http://hdl.handle.net/10077/3121.

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2007/2008
The intensity of natural light can span over 10 orders of magnitude from starlight to direct sunlight. Even in a single scene, the luminance of the bright areas can be thousands or millions of times greater than the luminance in the dark areas; the ratio between the maximum and the minimum luminance values is commonly known as dynamic range or contrast. The human visual system is able to operate in an extremely wide range of luminance conditions without saturation and at the same time it can perceive fine details which involve small luminance differences. Our eyes achieve this ability by modulating their response as a function of the local mean luminance with a process known as local adaptation. In particular, the visual sensation is not linked to the absolute luminance, but rather to its spatial and temporal variation. One consequence of the local adaptation capability of the eye is that the objects in a scene maintain their appearance even if the light source illuminating the scene changes significantly. On the other hand, the technologies used for the acquisition and reproduction of digital images are able to handle correctly a significantly smaller luminance range of 2 to 3 orders of magnitude at most. Therefore, a high dynamic range (HDR) image poses several challenges and requires the use of appropriate techniques. These elementary observations define the context in which the entire research work described in this Thesis has been performed. As indicated below, different fields have been considered; they range from the acquisition of HDR images to their display, from visual quality evaluation to medical applications, and include some developments on a recently proposed class of display equipment. An HDR image can be captured by taking multiple photographs with different exposure times or by using high dynamic range sensors; moreover, synthetic HDR images can be generated with computer graphics by means of physically-based algorithms which often involve advanced lighting simulations. An HDR image, although acquired correctly, can not be displayed on a conventional monitor. The white level of most devices is limited to a few hundred cd/m² by technological constraints, primarily linked to the power consumption and heat dissipation; the black level also has a non negligible luminance, in particular for devices based on the liquid crystal technology. However, thanks to the aforementioned properties of the human visual system, an exact reproduction of the luminance in the original scene is not strictly necessary in order to produce a similar sensation in the observer. For this purpose, dynamic range reduction algorithms have been developed which attenuate the large luminance variations in an image while preserving as far as possible the fine details. The most simple dynamic range reduction algorithms map each pixel individually with the same nonlinear function commonly known as tone mapping curve. One operator we propose, based on a modified logarithmic function, has a low computational cost and contains one single user-adjustable parameter. However, the methods belonging to this category can reduce the visibility of the details in some portions of the image. More advanced methods also take into account the pixel neighborhood. This approach can achieve a better preservation of the details, but the loss of one-to-one mapping from input luminances to display values can lead to the formation of gradient reversal effects, which typically appear as halos around the object boundaries. Different solutions to this problem have been attempted. One method we introduce is able to avoid the formation of halos and intrinsically prevents any clipping of the output display values. The method is formulated as a constrained optimization problem, which is solved efficiently by means of appropriate numerical methods. In specific applications, such as the medical one, the use of dynamic range reduction algorithms is discouraged because any artifacts introduced by the processing can lead to an incorrect diagnosis. In particular, a one-to-one mapping from the physical data (for instance, a tissue density in radiographic techniques) to the display value is often an essential requirement. For this purpose, high dynamic range displays, capable of reproducing images with a wide luminance range and possibly a higher bit depth, are under active development. Dual layer LCD displays, for instance, use two liquid crystal panels stacked one on top of the other over an enhanced backlight unit in order to achieve a dynamic range of 4 ÷ 5 orders of magnitude. The grayscale reproduction accuracy is also increased, although a “bit depth” can not be defined unambiguously because the luminance levels obtained by the combination of the two panels are partially overlapped and unevenly spaced. A dual layer LCD display, however, requires the use of complex splitting algorithms in order to generate the two images which drive the two liquid crystal panels. A splitting algorithm should compensate multiple sources of error, including the parallax introduced by the viewing angle, the gray-level clipping introduced by the limited dynamic range of the panels, the visibility of the reconstruction error, and glare effects introduced by an unwanted light scattering between the two panels. For these reasons, complex constrained optimization techniques are necessary. We propose an objective function which incorporates all the desired constraints and requirements and can be minimized efficiently by means of appropriate techniques based on multigrid methods. The quality assessment of high dynamic range images requires the development of appropriate techniques. By their own nature, dynamic range reduction algorithms change the luminance values of an image significantly and make most image fidelity metrics inapplicable. Some particular aspects of the methods can be quantified by means of appropriate operators; for instance, we introduce an expression which describes the detail attenuation introduced by a tone mapping curve. In general, a subjective quality assessment is preferably performed by means of appropriate psychophysical experiments. We conducted a set of experiments, targeted specifically at measuring the level of agreement between different users when adjusting the parameter of the modified logarithmic mapping method we propose. The experimental results show a strong correlation between the user-adjusted parameter and the image statistics, and suggest a simple technique for the automatic adjustment of this parameter. On the other hand, the quality assessment in the medical field is preferably performed by means of objective methods. In particular, task-based quality measures evaluate by means of appropriate observer studies the clinical validity of the image used to perform a specific diagnostic task. We conducted a set of observer studies following this approach, targeted specifically at measuring the clinical benefit introduced by a high dynamic range display based on the dual layer LCD technology over a conventional display with a low dynamic range and 8-bit quantization. Observer studies are often time consuming and difficult to organize; in order to increase the number of tests, the human observers can be partially replaced by appropriate software applications, known as model observers or computational observers, which simulate the diagnostic task by means of statistical classification techniques. This thesis is structured as follows. Chapter 1 contains a brief background of concepts related to the physiology of human vision and to the electronic reproduction of images. The description we make is by no means complete and is only intended to introduce some concepts which will be extensively used in the following. Chapter 2 describes the technique of high dynamic range image acquisition by means of multiple exposures. In Chapter 3 we introduce the dynamic range reduction algorithms, providing an overview of the state of the art and proposing some improvements and novel techniques. In Chapter 4 we address the topic of quality assessment in dynamic range reduction algorithms; in particular, we introduce an operator which describes the detail attenuation introduced by tone mapping curves and describe a set of psychophysical experiments we conducted for the adjustment of the parameter in the modified logarithmic mapping method we propose. In Chapter 5 we move to the topic of medical images and describe the techniques used to map the density data of radiographic images to display luminances. We point out some limitations of the current technical recommendation and propose an improvement. In Chapter 6 we describe in detail the dual layer LCD prototype and propose different splitting algorithms for the generation of the two images which drive the two liquid crystal panels. In Chapter 7 we propose one possible technique for the estimation of the equivalent bit depth of a dual layer LCD display, based on a statistical analysis of the quantization noise. Finally, in Chapter 8 we address the topic of objective quality assessment in medical images and describe a set of observer studies we conducted in order to quantify the clinical benefit introduced by a high dynamic range display. No general conclusions are offered; the breadth of the subjects has suggested to draw more focused comments at the end of the individual chapters.
XXI Ciclo
1982
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Silk, Simon. "High Dynamic Range Panoramic Imaging with Scene Motion." Thesis, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20394.

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Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.
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Urbano, António Carlos Alves. "Visualização de imagens HDR em dispositivos com ecrã pequeno." Doctoral thesis, Universidade de Trás-os-Montes e Alto Douro, 2013. http://hdl.handle.net/10400.8/976.

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Tese de Doutoramento em Informática apresentada à Universidade de Trás-os-Montes e Alto Douro em 2013.
Nas duas últimas décadas, assistimos ao desenvolvimento de um número crescente de técnicas, designadas por tone mapping operators (TMO), para reprodução de imagens com elevada gama dinâmica (high dynamic range – HDR) em ecrãs tradicionais. Apesar de recentemente terem surgido TMO que têm em conta a característica específica de cada dispositivo de visualização, nenhum desses algoritmos foi desenvolvido especificamente para dispositivos com ecrã pequeno (DEP). Assim, nesta tese foi realizado um estudo sobre a visualização de imagens HDR em DEP, tendo como propósito a proposta de soluções para a melhoria da sua visualização. Inicialmente foi realizada uma avaliação dos TMO atualmente existentes que mostrou que os DEP com tamanho limitado, resolução e profundidade de cor, exigem uma investigação específica para encontrar ou criar uma solução adequada. Esse estudo permitiu, também, identificar um conjunto de características dos TMO que precisam ser enfatizadas para obter imagens mapeadas com uma maior fidelidade nos DEP, especialmente o realce dos detalhes. Com base nesse estudo foi proposta uma solução para visualização de imagens HDR em DEP, que tem por base a construção de um TMO híbrido. O TMO proposto, tendo por base um qualquer TMO, tenta melhorar os detalhes das imagens mapeadas com o TMO original. Através da realização de experiências psicofísicas foi demonstrado que este novo TMO produz melhores resultados em DEP que os obtidos com o TMO original. Apesar de ter sido desenvolvido um protótipo em J2ME do novo TMO, a sua atual implementação ainda é pouco eficiente para a realização de testes diretamente em DEP. Em conclusão, com este trabalho é identificada a problemática da visualização de imagens HDR em DEP, sendo apontadas sugestões de como melhorar esse processo e fica ainda a proposta de um novo TMO.
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Lluis-Gomez, Alexis L. "Algorithms for the enhancement of dynamic range and colour constancy of digital images & video." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19580.

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One of the main objectives in digital imaging is to mimic the capabilities of the human eye, and perhaps, go beyond in certain aspects. However, the human visual system is so versatile, complex, and only partially understood that no up-to-date imaging technology has been able to accurately reproduce the capabilities of the it. The extraordinary capabilities of the human eye have become a crucial shortcoming in digital imaging, since digital photography, video recording, and computer vision applications have continued to demand more realistic and accurate imaging reproduction and analytic capabilities. Over decades, researchers have tried to solve the colour constancy problem, as well as extending the dynamic range of digital imaging devices by proposing a number of algorithms and instrumentation approaches. Nevertheless, no unique solution has been identified; this is partially due to the wide range of computer vision applications that require colour constancy and high dynamic range imaging, and the complexity of the human visual system to achieve effective colour constancy and dynamic range capabilities. The aim of the research presented in this thesis is to enhance the overall image quality within an image signal processor of digital cameras by achieving colour constancy and extending dynamic range capabilities. This is achieved by developing a set of advanced image-processing algorithms that are robust to a number of practical challenges and feasible to be implemented within an image signal processor used in consumer electronics imaging devises. The experiments conducted in this research show that the proposed algorithms supersede state-of-the-art methods in the fields of dynamic range and colour constancy. Moreover, this unique set of image processing algorithms show that if they are used within an image signal processor, they enable digital camera devices to mimic the human visual system s dynamic range and colour constancy capabilities; the ultimate goal of any state-of-the-art technique, or commercial imaging device.
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Bonnard, Jennifer. "Génération d'images 3D HDR." Thesis, Reims, 2015. http://www.theses.fr/2015REIMS014/document.

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L’imagerie HDR et l’imagerie 3D sont deux domaines dont l’évolution simultanée mais indépendante n’a cessé de croître ces dernières années. D’une part, l’imagerie HDR (High Dynamic Range) permet d’étendre la gamme dynamique de couleur des images conventionnelles dites LDR (Low Dynamic Range). D’autre part, l’imagerie 3D propose une immersion dans le film projeté avec cette impression de faire partie de la scène tournée. Depuis peu, ces deux domaines sont conjugués pour proposer des images ou vidéos 3D HDR mais peu de solutions viables existent et aucune n’est accessible au grand public. Dans ce travail de thèse, nous proposons une méthode de génération d’images 3D HDR pour une visualisation sur écrans autostéréoscopiques en adaptant une caméra multi-points de vue à l’acquisition d’expositions multiples. Pour cela, des filtres à densité neutre sont fixés sur les objectifs de la caméra. Ensuite, un appareillement des pixels homologues permet l’agrégation des pixels représentant le même point dans la scène acquise. Finalement, l’attribution d’une valeur de radiance est calculée pour chaque pixel du jeud’images considéré par moyenne pondérée des valeurs LDR des pixels homologues. Une étape supplémentaire est nécessaire car certains pixels ont une radiance erronée. Nous proposons une méthode basée surla couleur des pixels voisins puis deux méthodes basées sur la correction de la disparité des pixels dontla radiance est erronée. La première est basée sur la disparité des pixels du voisinage et la seconde sur la disparité calculée indépendamment sur chaque composante couleur. Ce pipeline permet la générationd’une image HDR par point de vue. Un algorithme de tone-mapping est ensuite appliqué à chacune d’elles afin qu’elles puissent être composées avec les filtres correspondants à l’écran autostéréoscopique considéré pour permettre la visualisation de l’image 3D HDR
HDR imaging and 3D imaging are two areas in which the simultaneous but separate development has been growing in recent years. On the one hand, HDR (High Dynamic Range) imaging allows to extend the dynamic range of traditionnal images called LDR (Low Dynamic Range). On the other hand, 3Dimaging offers immersion in the shown film with the feeling to be part of the acquired scene. Recently, these two areas have been combined to provide 3D HDR images or videos but few viable solutions existand none of them is available to the public. In this thesis, we propose a method to generate 3D HDR images for autostereoscopic displays by adapting a multi-viewpoints camera to several exposures acquisition.To do that, neutral density filters are fixed on the objectives of the camera. Then, pixel matchingis applied to aggregate pixels that represent the same point in the acquired scene. Finally, radiance is calculated for each pixel of the set of images by using a weighted average of LDR values. An additiona lstep is necessary because some pixels have wrong radiance. We proposed a method based on the color of adjacent pixels and two methods based on the correction of the disparity of those pixels. The first method is based on the disparity of pixels of the neighborhood and the second method on the disparity independently calculated on each color channel. This pipeline allows the generation of 3D HDR image son each viewpoint. A tone-mapping algorithm is then applied on each of these images. Their composition with filters corresponding to the autostereoscopic screen used allows the visualization of the generated 3DHDR image
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Books on the topic "High Dynamic Range images (HDR)"

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Davis, Harold. Creating HDR photos. New York: Amphoto Books, 2012.

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B, Hoefflinger, ed. High-dynamic-range (HDR) vision: Microelectronics, image processing, computer graphics. Berlin: Springer, 2007.

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B, Hoefflinger, ed. High-dynamic-range (HDR) vision: Microelectronics, image processing, computer graphics. Berlin: Springer, 2007.

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Concepcion, Rafael. The HDR book: Unlocking the secrets of high dynamic range photography. [United States]: Peachpit Press, 2011.

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McCann, John J. The art and science of HDR imaging. Chichester, West Sussex, U.K: Wiley, 2012.

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Hoefflinger, Bernd, ed. High-Dynamic-Range (HDR) Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-44433-6.

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Wagner, Reinhard. Profibuch HDR-Fotografie: [atemberaubende Bilder mit HDR-Effekt erstellen ; richtig belichten für perfekte HDR-Bilder ; HDR-Bilder per Software optimieren]. Poing: Franzis, 2010.

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Kircher, Ju rgen. DRI und HDR - das perfekte Bild: [inklusive DVD-ROM; von der Planung u ber die Aufnahme bis zur Fertigstellung; alles zur Ausru stung fu r die Erstellung eindrucksvoller DRI- und HDR-Fotos; Vorstellung geeigneter Software, Workshops, verwandte Techniken, das HDR-Panorama]. [Heidelberg]: mitp, 2008.

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A world in HDR. Berkeley, Calif. : London: New Riders ; Pearson Education [distributor], 2010.

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Practical HDRI: High dynamic range imaging for photographers. Santa Barbara, CA: Rocky Nook, 2008.

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Book chapters on the topic "High Dynamic Range images (HDR)"

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Banterle, Francesco, Alessandro Artusi, Kurt Debattista, and Alan Chalmers. "HDR Images Compression." In Advanced High Dynamic Range Imaging, 197–219. Second edition. | Boca Raton : Taylor & Francis, CRC Press,: A K Peters/CRC Press, 2017. http://dx.doi.org/10.1201/9781315119526-8.

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Myszkowski, Karol, Rafał Mantiuk, and Grzegorz Krawczyk. "HDR Image Quality." In High Dynamic Range Video, 35–40. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-79528-2_4.

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Sá, Asla M., Paulo Cezar Carvalho, and Luiz Velho. "HDR Reconstruction." In High Dynamic Range Image Reconstruction, 19–36. Cham: Springer International Publishing, 2007. http://dx.doi.org/10.1007/978-3-031-79522-0_4.

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Myszkowski, Karol, Rafał Mantiuk, and Grzegorz Krawczyk. "Representation of an HDR Image." In High Dynamic Range Video, 9–16. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-79528-2_2.

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Myszkowski, Karol, Rafał Mantiuk, and Grzegorz Krawczyk. "HDR Image and Video Acquisition." In High Dynamic Range Video, 17–33. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-79528-2_3.

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Myszkowski, Karol, Rafał Mantiuk, and Grzegorz Krawczyk. "HDR Display Devices." In High Dynamic Range Video, 89–97. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-79528-2_7.

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Banterle, Francesco, Alessandro Artusi, Kurt Debattista, and Alan Chalmers. "HDR Pipeline." In Advanced High Dynamic Range Imaging, 13–43. Second edition. | Boca Raton : Taylor & Francis, CRC Press,: A K Peters/CRC Press, 2017. http://dx.doi.org/10.1201/9781315119526-2.

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Banterle, Francesco, Alessandro Artusi, Kurt Debattista, and Alan Chalmers. "HDR Video Compression." In Advanced High Dynamic Range Imaging, 221–39. Second edition. | Boca Raton : Taylor & Francis, CRC Press,: A K Peters/CRC Press, 2017. http://dx.doi.org/10.1201/9781315119526-9.

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Sá, Asla M., Paulo Cezar Carvalho, and Luiz Velho. "HDRI Acquisition and Visualization." In High Dynamic Range Image Reconstruction, 37–41. Cham: Springer International Publishing, 2007. http://dx.doi.org/10.1007/978-3-031-79522-0_5.

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Banterle, Francesco, Alessandro Artusi, Kurt Debattista, and Alan Chalmers. "HDR Video Tone Mapping." In Advanced High Dynamic Range Imaging, 115–34. Second edition. | Boca Raton : Taylor & Francis, CRC Press,: A K Peters/CRC Press, 2017. http://dx.doi.org/10.1201/9781315119526-5.

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Conference papers on the topic "High Dynamic Range images (HDR)"

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Yoo, Hyun Jin, Kang Yeon Kim, Hoe Min Kim, Kang Su Park, Seung Joo Lee, Kwang Hee Ko, and Kwan H. Lee. "Color correction of high dynamic range images at HDR-level." In SIGGRAPH07: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/1280720.1280794.

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Gunawan, Irwan Prasetya, Ocarina Cloramidina, Salmaa Badriatu Syafa'ah, Guson Prasamuarso Kuntarto, and Berkah I. Santoso. "High Dynamic Range (HDR) Image Quality Assessment: A Survey." In International Conferences on Information System and Technology. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0009354900330040.

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Lo, Raymond Chun Hing, Steve Mann, Jason Huang, Valmiki Rampersad, and Tao Ai. "High dynamic range (HDR) video image processing for digital glass." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393347.2396525.

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Kumar, V. Abhinau, Shashank Gupta, Sai Sheetal Chandra, Shanmuganathan Raman, and Sumohana S. Channappayya. "No-reference quality assessment of tone mapped High Dynamic Range (HDR) images using transfer learning." In 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2017. http://dx.doi.org/10.1109/qomex.2017.7965668.

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Huang, Yu-Ming, Jui-Chiu Chiang, and Sau-Gee Chen. "HDR-AGAN: Ghost-Free High Dynamic Range Imaging with Attention Guided Adversarial Network." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897556.

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Wang, Hu, Mao Ye, Xiatian Zhu, Shuai Li, Ce Zhu, and Xue Li. "KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/196.

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Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly, we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https://github.com/wanghu178/KUNet.git.
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Banterle, Francesco, Alessandro Artusi, Alejandro Moreo, and Fabio Carrara. "Nor-Vdpnet: A No-Reference High Dynamic Range Quality Metric Trained On Hdr-Vdp 2." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191202.

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Natale, Donald J., Matthew S. Baran, and Richard L. Tutwiler. "High dynamic range (HDR) video processing for the exploitation of high bit-depth sensors in human-monitored surveillance." In 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2014. http://dx.doi.org/10.1109/aipr.2014.7041912.

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Xi, Lv, and Luo Ming Ronnier. "TWO-DIMENSIONAL COLOUR APPEARANCE SCALES FOR COLOUR STIMULI HAVING HIGH DYNAMIC RANGE." In CIE 2021 Conference. International Commission on Illumination, CIE, 2021. http://dx.doi.org/10.25039/x48.2021.op45.

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New colour appearance scales close to daily experience and image quality enhancement are highly desired including whiteness, blackness, vividness and depth. This article describes a new experiment to accumulate the data under HDR (high dynamic range) conditions. The data were then used to test the performance of different colour appearance scales such as CIELAB and CAM16-UCS plus the recent extension by Berns’ Vab*, Dab*. The results showed those Berns’ scales gave reasonable performance. However, there was no scale capable of predicting colour appearance data covering a wide dynamic range. New scales were developed based on the absolute scales of brightness and colourfulness of CAM16-UCS and gave accurate prediction to the data.
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Wang, Xinyang, Bram Wolfs, Jan Bogaerts, Guy Meynants, and Ali BenMoussa. "A high-dynamic range (HDR) back-side illuminated (BSI) CMOS image sensor for extreme UV detection." In IS&T/SPIE Electronic Imaging, edited by Ralf Widenhorn, Valérie Nguyen, and Antoine Dupret. SPIE, 2012. http://dx.doi.org/10.1117/12.906617.

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Reports on the topic "High Dynamic Range images (HDR)"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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