High Dynamic Range Display Adopting High Dynamic Range Imaging Technique

Perceptual brightness-based inverse tone mapping for high dynamic range imaging

Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less number of samples (compared to raster scanning). Our experimental results show that high dynamic range compressive imaging system is capable of capturing images with large intensity contrast.

High dynamic range imaging (HDRI) methods in computational photography address situations where the dynamic range of a scene exceeds what can be captured by an image sensor in a single exposure. HDRI techniques have also been used to construct radiance maps in measurement applications; unfortunately, the design and evaluation of HDRI algorithms for use in these applications have received little attention. In this paper, we develop a novel HDRI technique based on pixel-by-pixel Kalman filtering and evaluate its performance using objective metrics that this paper also introduces. In the presented experiments, this new technique achieves as much as 9.4-dB improvement in signal-to-noise ratio and can achieve as much as a 29% improvement in radiometric accuracy over a classic method.

High dynamic range imaging techniques involve capturing and storing real world radiance values that span many orders of magnitude. However, common display devices can usually reproduce intensity ranges only up to two to three orders of magnitude. Therefore, in order to display a high dynamic range image on a low dynamic range screen, the dynamic range of the image needs to be compressed without losing details or introducing artefacts, and this process is called tone mapping. A good tone mapping operator must be able to produce a low dynamic range image that matches as much as possible the perception of the real world scene. We propose a two stage tone mapping approach, in which the first stage is a global method for range compression based on a gamma curve that equalizes the lightness histogram the best, and the second stage performs local contrast enhancement and color induction using neural activity models for the visual cortex. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Multispectral single shot imaging systems can benefit computer vision applications in needs of a compact and affordable imaging system. Spectral filter arrays technology meets the requirement, but can lead to artifacts due to inhomogeneous intensity levels between spectral channels due to filter manufacturing constraints, illumination and object properties. One solution to solve this problem is to use high dynamic range imaging techniques on these sensors. We define a spectral imaging pipeline that incorporates high dynamic range, demosaicing and color image visualization. Qualitative evaluation is based on real images captured with a prototype of spectral filter array sensor in the visible and near infrared.

This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for feature point detection under extreme lighting conditions. The conditions are extreme in respect to the dynamic range of the lighting within the test scenes used. This dynamic range cannot be captured using standard low dynamic range imagery techniques without loss of detail. Four widely used feature point detectors are used in the experiments: Harris corner detector, Shi-Tomasi, FAST and Fast Hessian. Their repeatability rate is studied under changes of camera viewpoint, camera distance and scene lighting with respect to the image formats used. The results of the experiments show that HDR imaging techniques improve the repeatability rate of feature point detectors significantly.

The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, an efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images.

Current HDR display technology approaches the dynamic range of the fully adapted human vision system. As such, this technology has potential for performing as a surrogate for realworld scenes in the evaluation of the accuracy of high dynamic range (HDR) algorithms that map HDR scenes to the more common, everyday display technology having limited dynamic range. Clearly, use of HDR display technology has the benefit of simplicity in experimental design while maintaining the high dynamic range of the original scene. To evaluate this potential use for HDR displays, seven published versions of well-known tone mapping algorithms were benchmarked for rendering accuracy against each of four real-world scenes constructed in the lab and their corresponding renderings on the Munsell Color Science Laboratory's (MCSL) HDR display. The results between those obtained from the MCSL HDR display and those obtained from the real-world scenes are in good agreement thus validating the HDR display's potential as an evaluation tool in this context.

Physical or virtual models are commonly employed to visualize the conceptual ideas of architects, lighting designers and daylighting researchers. The models are also used to assess the daylighting performance of their buildings, particularly when Complex Fenestration Systems (CFS) are considered. Recent studies have revealed a general tendency of physical models to over- estimate the performance, usually expressed through work plane illuminance and daylight factor profiles, when compared to that of the real building. These discrepancies can be attributed to several experimental errors. To analyze the main sources of error, a set of comparisons between a real building, a virtual model and a physical model was undertaken. The real building in our case consisted of a full scale test module with a south-facing windows designed for experimentation on daylighting systems. A virtual model was a computed model created in Radiance program while the physical model was a scale model (1:10) of the real case. The fenestration systems considered in this study were a simple window (double glazing) and two CFS (Laser-cut panel and Prismatic film). The physical model was placed in outdoor conditions similar to that of the real building as well as under a scanning sky simulator (for both real sky luminance distribution and CIE standard sky); the virtual model simulations were carried out with the program Radiance using the GenSky function (for CIE standard sky) and the Partial Daylight Factor (PDF) method, the later using the real sky luminance distribution acquired by a digital sky scanner at the same time as the real building's daylight performance was assessed. The daylighting performances of the building, daylight factor (DF) for overcast sky and illuminance ratio (IR) for clear sky, were monitored using illuminance meters: a set of sensors for exterior illuminance and another set of equally spaced 7 sensors placed at 1m intervals starting from the window plane for the interior space were used for that purpose. The interior surface luminance of both real building and physical model was measured using a luminance meter and a High Dynamic Range (HDR) imaging technique (within the Photosphere program). The Radiance program was used to determine the interior surface luminance within the virtual model. The measured performance of the real case, physical models and virtual models were compared, the causes of discrepancies between the real building and models were analyzed. The causes of errors that were evaluated were modeling of building details and dimensions, CFS modeling, mocking-up of the photometric properties (surface reflectance and window transmittance), model location as well as photometer features. To study the impact of these error sources on daylighting performance assessment, virtual models created using the Radiance program were used to achieve a sensitivity analysis of modeling errors. The significant factors were considered, leading to a set of modeling guidelines. The experimental study shows that large discrepancies can occur in daylighting performance figures. For example if glazings are omitted from the model's window, a relative divergence of 25% to 40% can be found at different points in the room, suggesting more light entering than actually measured in the real building. Inaccuracy in window transmittance inaccuracy is a major cause of errors commonly found in daylight modeling. In addition, significant discrepancies can be caused by even slight error in surface reflectance values. Only 10% overestimation of surface reflectance modeling leads up to 80% relative errors in work plane illuminance for a simple window and up to 90% for the assessment of CFS. Continuous sky distribution presented more accurate results than 145 sky sectors simulation, particularly when CFS were evaluated. These discrepancies can be reduced by making an effort to mock up the geometric and photometric features including the daylight simulation of the models carefully. A checklist presented in this thesis can be used as a guideline to help the daylight designers to estimate and avoid errors when assessing daylighting performance.

Extended Exposure Fusion (EEF) is a high dynamic range imaging technique. It was recently proposed as an improvement of an earlier technique called Exposure Fusion (EF), itself widely used thanks to its very good results. But EF has two well-known artifacts: an out-of-range artifact and a low-frequency halo. The extended version solves them, and delivers fused results with enhanced contrast everywhere in the image. We give in this paper a precise description, analysis and implementation of the extended exposure fusion. We notably verify that the artifacts are removed and that the local contrast is improved compared to the original exposure fusion.

Some scenes and objects have a wide range of brightness that cannot be captured with a conventional camera. This limitation, which degrades the dynamic range of an imaged scene or object, is addressed by use of high dynamic range (HDR) imaging techniques. With HDR ima- ging techniques, images of a very broad range of intensity can be obtained with conventional cameras. Another limitation of conventional cameras is the range of wavelength that they can capture. Outside the visible wave- lengths, the responsivity of conventional cameras drops; therefore, a con- ventional camera cannot capture images in nonvisible wavelengths. Compressive imaging is a solution for this problem. Compressive imaging reduces the number of pixels of a camera to one, so a camera can be replaced by a detector with one pixel. The range of wavelengths to which such detectors are responsive is much wider than that of a conven- tional camera. A combination of HDR imaging and compressive imaging is introduced and is benefitted from the advantages of both techniques. An algorithm that combines these two techniques is proposed, and results are presented. © 2012 Society of Photo-Optical Instrumentation Engineers

In this paper we present a new technique for the display of High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as reference, that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO.

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

Feature Point (FP) detection is a fundamental step in computer vision tasks. Although FP detectors are mostly designed to support Low Dynamic Range (LDR) images as input, interest in High Dynamic Range (HDR) images has increased recently due to their higher precision to register overexposed and underexposed areas in an image. As the detection of FPs is strongly dependent on the illumination of the scene, HDR images have the potential to be more robust than LDR images during FP detection. Known FP detectors, however, do not use the full potential of HDR images. In addition, few works have evaluated the performance of HDR images in this context. In this paper, we propose a modification of FP detectors aiming to improve FP detection on HDR images. To this end, we design a local mask based on the Coefficient of Variation (CV) of sets of pixels, creating thus a new step in FP detection. We compare our approach with popular FP detection methods using a standard evaluation metric, Repeatability Rate (RR) of FPs, and also Uniformity as a proposed new criterion. A dataset of images from scenes affected by camera transformations and substantial illumination changes was used as input. Experimental results show that our proposed algorithms give better Uniformity and RR in most HDR images from the dataset when compared to standard FP detectors. Moreover, they indicate that HDR images present a great potential to be explored in applications that rely on FP detection.

Recently, visual representations using high dynamic range (HDR) images become increasingly popular, with advancement of technologies for increasing the dynamic range of image. HDR image is expected to be used in wide-ranging applications such as digital cinema, digital photography and next generation broadcast, because of its high quality and its powerful expression ability. HDR imaging technologies will spread its sphere of influence in imaging industry. In this paper, we review the state-of-the-art studies and the trends of the HDR imaging, in terms of the following three points: (1) HDR imaging sensor and HDR image generation techniques as image acquisition technologies, (2) encode method of HDR images for efficient transmission and storage, (3) human visual system issues associated with reproduction of HDR image.