High dynamic range compressive imaging: a programmable imaging system

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.

Technique of high dynamic range imaging (HDRI) was introduced into conventional high dynamic range display (HDRD). Sharpness of image was further enhanced by improving local contrast ratio in the HDRI-based high dynamic range display.

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

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.

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.

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.

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.

This paper describes a new method to automatically improve scene lighting for video conferencing by learning the photometric mapping between a lower exposure and desired exposure created using High Dynamic Range (HDR) imaging techniques. Once the mapping is learned in a calibration step, it can be used to transform all subsequent images, effectively producing higher dynamic range video without ghosting artifacts. A stereo algorithm is also described that allows multiple exposures to be taken at every frame, useful if the lighting of a scene changes significantly. Results show that this is an effective way to improve face lighting and therefore the overall experience of video conferencing.

Typical digital cameras exhibit a limitation regarding the dynamic range of the scene radiance they can capture. High Dynamic Range (HDR) imaging refers to methods and systems that aim to generate images that exhibit higher dynamic range between the lightest and the darkest parts of the an image. A typical approach for generating HDR images is exposure bracketing where multiple frames, each one with a different exposure setting, are captured and combined to a HDR image of the scene. The large number of images that exposure bracketing requires often leads to motion artefacts that limit the visual quality of the resulting HDR image. In this work, we propose a novel approach in HDR imaging that significantly reduces the necessary number of images. In our proposed system, we employ the notion of random exposure where each pixel of a single frame collects light for a random amount of time. By collecting a small number of such images, the full sequence of low dynamic range images can be reconstructed and subsequently used for HDR generation. The problem is solved by casting the reconstruction of the sequence as a nuclear norm minimization problem following the premises of the recently proposed theory of Matrix Completion. Experimental results suggest that the proposed method is able to reconstruct the sequence from as low as 20% of the images that traditional techniques require with minimal reduction in image quality.

High dynamic range (HDR) images are highly desirable and widely applied in our daily life. However, most conventional digital cameras are only able to capture low dynamic range (LDR) images. To produce HDR images, a popular and effective method is to merge multiple LDR images with different exposure settings. To achieve high quality HDR images, determining the exposure settings when capturing the multiple LDR images is of great importance. Though many researchers have paid attention to the exposure determination issue for HDR imaging, most of them have focused on DSLR cameras and surveillance video cameras. Meanwhile, mobile devices nowadays have increasing use but few studies in literature focus on them. Since cameras of mobile devices have some features different from DSLR cameras and surveillance video cameras, many existing exposure determination methods may not be directly applied on them. To fill this research gap, this paper proposes a dynamic exposure determination method for HDR photography on mobile devices, in particular on iOS mobile devices. In the proposed method, three LDR images are captured on iOS devices, which are then merged to generate an HDR result image that can cover more details of the target scene. Considering the time and memory limits of mobile devices, the proposed method analyzes the histogram of the target scene thumbnail to determine the three exposure settings with suitable exposure gaps. Based on our proposed method, we have developed an iOS HDR Camera App. The experimental results demonstrate that the HDR result images of this App are capable to contain more details in both bright area and dark area than those of Apple's default HDR Camera App on iPhone 5s for real-world target scenes.

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.

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.

High Dynamic Range (HDR) images capture the full range of luminance present in real world scenes, and unlike Low Dynamic Range (LDR) images, can simultaneously contain detailed information in the deepest of shadows and the brightest of light sources. In order to render HDR image on LDR displayers, it is often necessary to create LDR depictions of HDR images at the cost of contrast information loss. To reduce the loss, this paper enables to render HDRI (High Dynamic Range Image) with multiple low-bit images periodically. From the viewpoint of a human, the pixel value is fractural. It does not adjust the tones but can reconstruct HDR images.

In this paper, we introduce the notion of a programmable imaging system. Such an imaging system provides a human user or a vision system significant control over the radiometric and geometric characteristics of the system. This flexibility is achieved using a programmable array of micro-mirrors. The orientations of the mirrors of the array can be controlled with high precision over space and time. This enables the system to select and modulate rays from the scene's light field based on the needs of the application at hand. We have implemented a programmable imaging system that uses a digital micro-mirror device (DMD), which is used in digital light processing. Although the mirrors of this device can only be positioned in one of two states, we show that our system can be used to implement a wide variety of imaging functions, including, high dynamic range imaging, feature detection, and object recognition. We also describe how a micro-mirror array that allows full control over the orientations of its mirrors can be used to instantly change the field of view and resolution characteristics of the imaging system. We conclude with a discussion on the implications of programmable imaging for computer vision.