Biological imaging with high dynamic range using compressive imaging technique

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.

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

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

A number of successful tone mapping operators for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range devices. They were inspired by fields as diverse as image processing, photographic practice, and modeling of the human visual systems (HVS). The variety of approaches calls for a systematic perceptual evaluation of their performance. We conduct a psychophysical experiment based on a direct comparison between the appearance of real-world scenes and HDR images of these scenes displayed on a low dynamic range monitor. In our experiment, HDR images are tone mapped by seven existing tone mapping operators. The primary interest of this psychophysical experiment is to assess the differences in how tone mapped images are perceived by human observers and to find out which attributes of image appearance account for these differences when tone mapped images are compared directly with their corresponding real-world scenes rather than with each other. The human subjects rate image naturalness, overall contrast, overall brightness, and detail reproduction in dark and bright image regions with respect to the corresponding real-world scene. The results indicate substantial differences in perception of images produced by individual tone mapping operators. We observe a clear distinction between global and local operators in favor of the latter, and we classify the tone mapping operators according to naturalness and appearance attributes.

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 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.

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.

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.

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.

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.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

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.

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.

In this paper, we propose a bit-depth scalable lossless coding method for high dynamic range (HDR) images based on a reversible logarithmic mapping. HDR images are generally expressed as floating-point data, such as in the OpenEXR or RGBE formats. Our bit-depth scalable coding approach outputs base layer data and enhancement layer data. It can reconstruct the low dynamic range (LDR) image from the base layer data and reconstructs the HDR image by adding the enhancement layer data. Most previous two-layer methods have focused on the lossy coding of HDR images. Unfortunately, the extension of previous lossy methods to lossless coding does not significantly compress the enhancement layer data. This is because the bit depth becomes very large, especially for HDR images in floating-point data format. To tackle this problem, we apply a reversible logarithmic mapping to the input HDR data. Moreover, we introduce a format conversion to avoid any degradation in the quality of the reconstructed LDR image. The proposed method is effective for both OpenEXR and RGBE formats. Through a series of experiments, we confirm that the proposed method decreases the volume of compressed data while maintaining the visual quality of the reconstructed LDR images.