Showing papers on "High-dynamic-range imaging published in 2013"

Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality

Abstract: We study the issue of quality assessment in tone mapping- based high-dynamic-range (HDR) image compression. In this, there are two stages at which a decision should be made regarding perceptual vis- ual quality: (a) for finding the optimal parameters of the dynamic range reduction function so that the visual quality is maximized, and (b) visual quality judgment of the decompressed image. We first investigate two objective optimization criteria, namely mean squared error and structural similarity index measure, toward optimization of a tone mapping model- based HDR image compression method. We then conduct a comprehen- sive subjective study to evaluate the visual quality of the compressed HDR images. Therefore, we consider both objective and subjective aspects for HDR image compression. To our knowledge, no systematic and compre- hensive studies exist in the current literature which shed light on the issue of quality assessment in HDR compression. So this study brings in new knowledge and perspective for the relatively less investigated topic of HDR compression from the view point of perceptual quality. We further evaluate the prediction performances of four objective methods on the 140 compressed HDR images that have been subjectively rated.

Fibonacci Exposure Bracketing for High Dynamic Range Imaging

Abstract: Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.

High Dynamic Range Imaging

Abstract: While real scenes produce a wide range of brightness variations, current cameras use low dynamic range image detector that typically provide 256 levels of brightness data at each pixel. We propose methods to create High Dynamic Range images, the method to enhance the dynamic range of is based on capturing multiple exposure photographs of the scene. Even if there are few methods available for creating HDR images, HDR display technologies still lag behind. We present a tone mapping algorithm to display HDR images on Low Dynamic Range display devices. We also deal with histogram manipulation for painterly effect on output. Applications of HDR images are discussed in brief. An algorithm to create HDR images for still scenes in Color Filter Array (CFA) domain is proposed and successfully implemented.

Simulating high dynamic range imaging with virtual long-exposure images

Abstract: A total exposure time (TET) may be selected. A plurality of images of a scene may be captured using respective TETs that are based on the selected TET. At least two of the images in the plurality of images may be combined to form a merged short-exposure image. A digital gain may be applied to the merged short-exposure image to form a virtual long-exposure image. The merged short-exposure image and the virtual long-exposure image may be combined to form an output image. More of the output image may be properly-exposed than either of the merged short-exposure image or the virtual long-exposure image.

High dynamic range imaging by a rank-1 constraint

Abstract: We present a high dynamic range (HDR) imaging algorithm that utilizes a modern rank minimization framework. Linear dependency exists among low dynamic range (LDR) images. However, global or local misalignment by camera motion and moving objects breaks down the low-rank structure of LDR images. The proposed algorithm simultaneously estimates global geometric transforms to align LDR images and detects moving objects and under-/over-exposed regions using a rank minimization approach. In the HDR composition step, structural consistency weighting is proposed to generate an artifact-free HDR image from an user-selected reference image. We demonstrate the robustness and effectiveness of the proposed method with real datasets.

High Dynamic Range Imaging: Sensors and Architectures

Abstract: Illumination is a crucial element in many applications, matching the luminance of the scene with the operational range of a camera. When luminance cannot be adequately controlled, a high dynamic range (HDR) imaging system may be necessary. These systems are being increasingly used in automotive on-board systems, road traffic monitoring, and other industrial, security, and military applications. This book provides readers with an intermediate discussion of HDR image sensors and techniques for industrial and non-industrial applications. It describes various sensor and pixel architectures capable of achieving HDR imaging, as well as software approaches to make high dynamic range images out of lower dynamic range sensors or image sets. Some methods for automatic control of exposure and dynamic range of image sensors are also introduced.

Methods, systems, and media for high dynamic range imaging

Abstract: Systems, methods, and media for high dynamic range imaging are provided, the systems comprising: an image sensor; and a hardware processor configured to: cause the image sensor to capture first image data having a first exposure time, second image data having a second exposure time, and third image data having a third exposure time that is substantially equal to the sum of the first exposure time and the second exposure time; generate combined image data using the first image data and the second image data.

HDR image multi-bit watermarking using bilateral-filtering-based masking

Abstract: The present paper proposes a blind multi-bit watermarking method for High Dynamic Range (HDR) images. The proposed approach is designed in order to guarantee the watermark imperceptibility both in the HDR marked image and in its Low Dynamic Range (LDR) counterpart, being thus robust against significant non-linear distortions such as those performed by tone-mapping operators (TMOs). In order to do so, the wavelet transform of the Just Noticeable Difference (JND)- scaled space of the original HDR image is employed as embedding domain. Moreover, a visual mask taking into account specific aspects of the Human Visual System (HVS) is exploited to improve the quality of the resulting watermarked image. Specifically, bilateral filtering is used to locate information on the detail part of the HDR image, where the watermark should be preferably embedded. A contrast sensitivity function is also employed to modulate the watermark intensity in each wavelet decomposition subband according to its scale and orientation. An extensive set of experimental results testifies the effectiveness of the proposed scheme in embedding multi-bit watermarks into HDR images without affecting the visual quality of the original image, while being robust against TMOs.

Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging

Abstract: High dynamic range (HDR) images can be generated by capturing a sequence of low dynamic range (LDR) images of the same scene with different exposures and then merging those images to create an HDR image. During capturing of LDR images, any changes in the scene or slightest camera movement results in ghost artifacts in the resultant HDR image. Over the past few years many algorithms have been proposed to produce ghost free HDR images of dynamic scenes. In this study we performed subjective psychophysical experiments to evaluate four algorithms for removing ghost artifacts in the final HDR image. To our best knowledge, no evaluation of deghosting algorithms for HDR imaging has been published. Thus, the aim of this paper is not only to evaluate different ghost removal algorithms but also to introduce a methodology to evaluate such algorithms and to present some of the challenges that exist in evaluating ghost removal algorithms in HDR images. Optical flow algorithms have been shown to produce successful results in aligning input images before merging them into an HDR image. As a result one of the state-of-the-art deghosting algorithm for HDR image alignment is based on optical flow. To test the limits of the evaluated deghosting algorithms the scenes used in our experiments were selected following the criteria proposed by Baker et al. [2011], which is considered as de facto standard for evaluating optical flow methodologies. The scenes used in the experiments serve to provide challenges that need to be dealt with by not only algorithms based on optical flow methodologies but also by other ghost removal algorithms for HDR imaging. The results reveal the scenes for which the evaluated algorithms fail and may serve as a guide for future research in this area.

Non-Uniform Deblurring in HDR Image Reconstruction

Abstract: Hand-held cameras inevitably result in blurred images caused by camera-shake, and even more so in high dynamic range imaging applications where multiple images are captured over a wide range of exposure settings. The degree of blurring depends on many factors such as exposure time, stability of the platform, and user experience. Camera shake involves not only translations but also rotations resulting in nonuniform blurring. In this paper, we develop a method that takes input non-uniformly blurred and differently exposed images to extract the deblurred, latent irradiance image. We use transformation spread function (TSF) to effectively model the blur caused by camera motion. We first estimate the TSFs of the blurred images from locally derived point spread functions by exploiting their linear relationship. The scene irradiance is then estimated by minimizing a suitably derived cost functional. Two important cases are investigated wherein 1) only the higher exposures are blurred and 2) all the captured frames are blurred.

Context-dependent JPEG backward-compatible high-dynamic range image compression

Abstract: High-dynamic range (HDR) imaging is expected, together with ultrahigh definition and high-frame rate video, to become a technology that may change photo, TV, and film industries. Many cameras and displays capable of capturing and rendering both HDR images and video are already available in the market. The popularity and full-public adoption of HDR content is, however, hindered by the lack of standards in evalu- ation of quality, file formats, and compression, as well as large legacy base of low-dynamic range (LDR) displays that are unable to render HDR. To facilitate the wide spread of HDR usage, the backward compatibility of HDR with commonly used legacy technologies for storage, rendering, and compression of video and images are necessary. Although many tone-mapping algorithms are developed for generating viewable LDR con- tent from HDR, there is no consensus of which algorithm to use and under which conditions. We, via a series of subjective evaluations, demonstrate the dependency of the perceptual quality of the tone-mapped LDR images on the context: environmental factors, display parameters, and image con- tent itself. Based on the results of subjective tests, it proposes to extend JPEG file format, the most popular image format, in a backward compat- ible manner to deal with HDR images also. An architecture to achieve such backward compatibility with JPEG is proposed. A simple implementation of lossy compression demonstrates the efficiency of the proposed archi- tecture compared with the state-of-the-art HDR image compression. © 2013

Generating stereoscopic HDR images using HDR-LDR image pairs

Abstract: A number of novel imaging technologies have been gaining popularity over the past few years. Foremost among these are stereoscopy and high dynamic range (HDR) Imaging. While a large body of research has looked into each of these imaging technologies independently, very little work has attempted to combine them. This is mostly due to the current limitations in capture and display. In this article, we mitigate problems of capturing Stereoscopic HDR (SHDR) that would potentially require two HDR cameras, by capturing an HDR and LDR pair and using it to generate 3D stereoscopic HDR content. We ran a detailed user study to compare four different methods of generating SHDR content. The methods investigated were the following: two based on expanding the luminance of the LDR image, and two utilizing stereo correspondence methods, which were adapted for our purposes. Results demonstrate that one of the stereo correspondence methods may be considered perceptually indistinguishable from the ground truth (image pair captured using two HDR cameras), while the other methods are all significantly distinct from the ground truth.

High dynamic range imaging on mobile devices using fusion of multiexposure images

Abstract: Because the real world scenes have a high dynamic range which exceeds the range of the imaging devices, the captured images sometimes contain under-exposed and saturated regions. In this paper, we propose a simple but effective method to achieve high dynamic range (HDR) rendering results from three multiexposure images comprising under-, normal-, and over-exposure. First, we generate the weight function, for the fusion of multiexposure images, according to the brightness. Then, we employ the bilateral filter-based retouching to enhance image details, especially in the dark regions. Experimental results demonstrate that the proposed method produces clear details in images and achieves natural HDR rendering results on mobile imaging devices.

High Dynamic Range Imaging Concept-Based Signal Enhancement Method Reduced the Optical Coherence Tomography Measurement Variability

Abstract: Purpose. To develop and test a novel signal enhancement method for optical coherence tomography (OCT) images based on the high dynamic range (HDR) imaging concept.

Dynamic range reduction and contrast adjustment of infrared images in surveillance scenarios

Abstract: The high thermal sensitivity of modern infrared (IR) cameras allows us to distinguish objects with small temperature variations. In comparison with the dynamics of standard displays, the sensed IR images have a high dynamic range (HDR). In this context, suitable techniques to display HDR images are required in order to improve the visibility of the details without introducing distortions. In the recent literature of IR image processing, a common framework to perform HDR image visualization relies on DR reduction (DRR) with a cascaded processing for local contrast adjustment (CA). In this work, a novel method, named cluster-based DRR and contrast adjustment (CDCA) is introduced for the visualization of IR images. The CDCA method is composed of two cascaded steps: (1) DRR clustering-based approach and (2) a CA module specifically designed to account for IR image features. The effectiveness of the introduced technique is analyzed using IR images of surveillance scenarios collected in different operating conditions. The results are compared with those given by other IR-HDR visualization methods and show the benefits of the proposed CDCA in terms of details enhancement, robustness against the horizon effect and presence of hot objects.

Comparametric HDR (High Dynamic Range) imaging for digital eye glass, wearable cameras, and sousveillance

Abstract: Wearable computing can be used to both extend the range of human perception, and to share sensory experiences with others. For this objective to be made practical, engineering considerations such as form factor, computational power, and power consumption are critical concerns. In this work, we consider the design of a low-power visual seeing aid, and how to implement computationally-intensive computational photography algorithms in a small form factor with low power consumption. We present realtime an FPGA-based HDR (High Dynamic Range) video processing and filtering by integrating tonal and spatial information obtained from multiple different exposures of the same subject matter. In this embodiment the system captures, in rapid succession, sets of three exposures, “dark”, “medium”, and “light”, over and over again, e.g. “dark”, “medium”, “light”, “dark”, “medium”, “light”, and so on, at 60 frames per second. These exposures are used to determine an estimate of the photoquantity every 1/60th of a second (each time a frame comes in, an estimate goes out). This allows us to build a seeing aid that helps people see better in high contrast scenes, for example, while welding, or in outdoor scenes, or scenes where a bright light is shining directly into the eyes of the wearer. Our system is suitable for being built into eyeglasses or small camera-based, lifeglogging, or gesture-sensing pendants, and other miniature wearable devices, with low-power and compact circuits that can be easily mounted on the body.

Adapting iterative retinex computation for high-dynamic-range tone mapping

Abstract: Retinex algorithms have been widely applied in many aspects of image processing. Based on the iterative Retinex algorithm, we propose an edge-preserving illumination estimation method. Inspired by the anisotropic diffusion, an edge-stopping function is introduced in the iterative computation. This modification enables the preservation of abrupt edges when computing the upper envelope of a given image. Based on the illumination-reflectance decomposition, a high-dynamic-range (HDR) radiance map can be easily tone-mapped to be a low-dynamic-range image by compressing the range of the estimated illumination. Artifacts are effectively suppressed using the proposed method. Meanwhile, we also propose a jumping-spiral iteration manner to improve the symmetry of the edge response. Experimental results show that the proposed tone mapping algorithm is very effective in reproducing HDR scenes, and has a better performance compared with some similar operators.

Image Quality Assessment using a High Dynamic Range Display

Abstract: In High Dynamic Range imaging (HDR), a remarkable range of compression algorithms has been provided in recent years. With the emergence of new HDR display technology, HDR content can now be displayed directly. We develop a colorimetrically calibrated viewing environment to evaluate the perceived quality of HDR and compressed LDR images. The goal is to use an HDR display instead of the original scenes. Our results give judgment agreement between the original scene and its HDR image version on a HDR display. Furthermore, we investigate whether visual comparisons of simultaneously displayed HDR and LDR content are feasible. We confirm that this set-up is of great use in the development of Tone Mapping Operators on the basis of an applied visual experiment.

Feature based ghost removal in high dynamic range imaging

Abstract: This paper presents a technique to reduce the ghost artifacts in a high dynamic range (HDR) image. In HDR imaging, we need to detect the motion between multiple exposure images of the same scene in order to prevent the ghost artifacts. First, we establish correspondences between the aligned reference image and the other exposure images using the zero-mean normalized cross correlation (ZNCC). Then, we find object motion regions using adaptive local thresholding of ZNCC feature maps and motion map clustering. In this process, we focus on finding accurate motion regions and on reducing false detection in order to minimize the side effects as well. Through experiments with several sets of low dynamic range images captured with different exposures, we show that the proposed method can remove the ghost artifacts better than existing methods.

Joint focus stacking and high dynamic range imaging

Abstract: Focus stacking and high dynamic range (HDR) imaging are two paradigms of computational photography. Focus stacking aims to produce an image with greater depth of field (DOF) from a set of images taken with different focus distances, whereas HDR imaging aims to produce an image with higher dynamic range from a set of images taken with different exposure settings. In this paper, we present an algorithm which combines focus stacking and HDR imaging in order to produce an image with both higher dynamic range and greater DOF than any of the input images. The proposed algorithm includes two main parts: (i) joint photometric and geometric registration and (ii) joint focus stacking and HDR image creation. In the first part, images are first photometrically registered using an algorithm that is insensitive to small geometric variations, and then geometrically registered using an optical flow algorithm. In the second part, images are merged through weighted averaging, where the weights depend on both local sharpness and exposure information. We provide experimental results with real data to illustrate the algorithm. The algorithm is also implemented on a smartphone with Android operating system.

Image data aggregating high dynamic range imaging systems and associated methods

Abstract: An image data aggregating high dynamic range imaging system includes an image sensor for generating N image data sets from an array of photodiodes, where N is an integer greater than one. The image sensor is adapted to generate each of the N image data sets with a different respective exposure time duration of the array of photodiodes. The system further includes an image data aggregating module for aggregating the N image data sets to obtain a virtual long exposure image data set.

Stereoscopic high dynamic range imaging

Abstract: Two modern technologies show promise to dramatically increase immersion in virtual environments Stereoscopic imaging captures two images representing the views of both eyes and allows for better depth perception High dynamic range (HDR) imaging accurately represents real world lighting as opposed to traditional low dynamic range (LDR) imaging HDR provides a better contrast and more natural looking scenes The combination of the two technologies in order to gain advantages of both has been, until now, mostly unexplored due to the current limitations in the imaging pipeline This thesis reviews both fields, proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the challenges that need to be resolved to enable SHDR and focuses on capture and compression aspects of that pipeline The problems of capturing SHDR images that would potentially require two HDR cameras and introduce ghosting, are mitigated by capturing an HDR and LDR pair and using it to generate SHDR images A detailed user study compared four different methods of generating SHDR images Results demonstrated that one of the methods may produce images perceptually indistinguishable from the ground truth Insights obtained while developing static image operators guided the design of SHDR video techniques Three methods for generating SHDR video from an HDR-LDR video pair are proposed and compared to the ground truth SHDR videos Results showed little overall error and identified a method with the least error Once captured, SHDR content needs to be efficiently compressed Five SHDR compression methods that are backward compatible are presented The proposed methods can encode SHDR content to little more than that of a traditional single LDR image (18% larger for one method) and the backward compatibility property encourages early adoption of the format The work presented in this thesis has introduced and advanced capture and compression methods for the adoption of SHDR imaging In general, this research paves the way for a novel field of SHDR imaging which should lead to improved and more realistic representation of captured scenes

Spatial imaging in color and HDR: prometheus unchained

Abstract: The Human Vision and Electronic Imaging Conferences (HVEI) at the IS and T/SPIE Electronic Imaging meetings have brought together research in the fundamentals of both vision and digital technology. This conference has incorporated many color disciplines that have contributed to the theory and practice of today's imaging: color constancy, models of vision, digital output, high-dynamic-range imaging, and the understanding of perceptual mechanisms. Before digital imaging, silver halide color was a pixel-based mechanism. Color films are closely tied to colorimetry, the science of matching pixels in a black surround. The quanta catch of the sensitized silver salts determines the amount of colored dyes in the final print. The rapid expansion of digital imaging over the past 25 years has eliminated the limitations of using small local regions in forming images. Spatial interactions can now generate images more like vision. Since the 1950's, neurophysiology has shown that post-receptor neural processing is based on spatial interactions. These results reinforced the findings of 19th century experimental psychology. This paper reviews the role of HVEI in color, emphasizing the interaction of research on vision and the new algorithms and processes made possible by electronic imaging.

High-dynamic-range image rendering using hybrid tone mapping and automatic k factor decision

Abstract: High-dynamic-range (HDR) imaging has become more and more popular in recent years, and a number of methods for the realistic representation of natural scenes have been proposed. Gradient domain HDR compression is one of the most popular approaches; however, their results are not satisfactory due to the parameter setting. Motivated by perceptual studies on image quality, we propose a novel HDR compression method to achieve better image quality. In the proposed method, we combine global and local tone-mapping operators, and manipulate the HDR compression in both luminance and gradient domains to improve the appearance and naturalness of results. Moreover, we deal with the parameter setting problem effectively in HDR compression by automatic k factor decision. Extensive experiments on various test images demonstrate that the proposed method is very effective in enhancing the overall quality of the image as well as maintaining the details without the loss of naturalness.

Tone mapping operator for high dynamic range imaging

Abstract: This paper presents a new tone mapping operator used in high dynamic range imaging system. The new operator combines the photographic tone mapping function and two new additional items which are based on the local contrast map and the smoothed map. The images processed through the proposed algorithm have better brightness, contrast, and visibility without producing undesired artifacts.

Motion adaptive signal integration-high dynamic range (MASI-HDR) video processing for dynamic platforms

Abstract: Two of the biggest challenges in designing U×V vision systems are properly representing high dynamic range scene content using low dynamic range components and reducing camera motion blur. SRI’s MASI-HDR (Motion Adaptive Signal Integration-High Dynamic Range) is a novel technique for generating blur-reduced video using multiple captures for each displayed frame while increasing the effective camera dynamic range by four bits or more. MASI-HDR processing thus provides high performance video from rapidly moving platforms in real-world conditions in low latency real time, enabling even the most demanding applications on air, ground and water.

High dynamic range imaging and local adaptive tone mapping

Abstract: We recently proposed a negative-feedback reset as a noise cancellation technique and showed that it can obtain intermediate image data during the charge-accumulation process. As an application, dynamic range compression is achieved by individually selecting pixels and by setting an intermediate voltage or performing quasi-holding with respect to each pixel. Additionally, we achieved dynamic range extending using multi-intermediate image readout. Since obtained images have global characteristics in their tone curves, local contrasts are degraded with simple HDR compression. Therefore local adaptive HDR compression is required. We propose an approach to generating a tone mapping function by using composite multiple gamma functions derived from local histograms. The multiple gamma functions represent the shadow, middle, and high-light tones of local-image areas. We can intuitively control the parameters of these three tones and suppress over-enhancement of images with LHE by using these multiple gamma functions. Moreover, we achieved two million pixels per 0.3 sec of operation and real-time movie correction (resolution: VGA, frame rate of 30fps) without the need for dedicated instructions or additional hardware by calculating the local histogram independent of the pixel-box size and using a look-up table for functions.

Measurement of camera response function based on polarizer

Abstract: Measurement of camera response function (CRF) has become an important technique in the field of computer graphics and radiance measurement. It can be used to achieve accurate modeling and high dynamic range imaging. A method was developed in this paper by using a polariscope to provide accurate radiance for direct measurement of the CRF. The experimental results show that the accuracy of the estimated CRF obtained by the new approach is about 5% better than that of the previous method.

Digital Holography Using High Dynamic-Range Imaging

Abstract: We propose a technique for improving the quality of a reconstructed image degraded due to the insufficiency of dynamic range of an image sensor in digital holography. We numerically confirm the effectiveness of the technique.