Rendering HDR images

Color imaging systems are continuously improving, and have now improved to the point of capturing high dynamic range scenes. Unfortunately most commercially available color display devices, such as CRTs and LCDs, are limited in their dynamic range. It is necessary to tone-map, or render, the high dynamic range images in order to display them onto a lower dynamic range device. This paper describes the use of an image appearance model, iCAM, to render high dynamic range images for display. Image appearance models have greater flexibility over dedicated tone-scaling algorithms as they are designed to predict how images perceptually appear, and not designed for the singular purpose of rendering. In this paper we discuss the use of an image appearance framework, and describe specific implementation details for using that framework to render high dynamic range images

Logarithmic Intensity Compression in Fluorescence Guided Surgery Applications

The use of fluorescence video imaging to guide surgery is rapidly expanding, and improvements in camera readout dynamic range have not matched display capabilities. Logarithmic intensity compression is a fast, single-step mapping technique that can map the useable dynamic range of high-bit fluorescence images onto the typical 8-bit display and potentially be a variable dynamic contrast enhancement tool. We demonstrate a ∼4.6  times improvement in image quality quantified by image entropy and a dynamic range reduction by a factor of ∼380 by the use of log-compression tools in processing in vivo fluorescence images

Reproducing reality with a high-dynamic-range multi-focal stereo display

With well-established methods for producing photo-realistic results, the next big challenge of graphics and display technologies is to achieve perceptual realism --- producing imagery indistinguishable from real-world 3D scenes. To deliver all necessary visual cues for perceptual realism, we built a High-Dynamic-Range Multi-Focal Stereo Display that achieves high resolution, accurate color, a wide dynamic range, and most depth cues, including binocular presentation and a range of focal depth. The display and associated imaging system have been designed to capture and reproduce a small near-eye three-dimensional object and to allow for a direct comparison between virtual and real scenes. To assess our reproduction of realism and demonstrate the capability of the display and imaging system, we conducted an experiment in which the participants were asked to discriminate between a virtual object and its physical counterpart. Our results indicate that the participants can only detect the discrepancy with a probability of 0.44. With such a level of perceptual realism, our display apparatus can facilitate a range of visual experiments that require the highest fidelity of reproduction while allowing for the full control of the displayed stimuli.</jats:p

High-fidelity colour reproduction for high-dynamic-range imaging

The aim of this thesis is to develop a colour reproduction system for high-dynamic-range (HDR) imaging. Classical colour reproduction systems fail to reproduce HDR images because current characterisation methods and colour appearance models fail to cover the dynamic range of luminance present in HDR images. HDR tone-mapping algorithms have been developed to reproduce HDR images on low-dynamic-range media such as LCD displays. However, most of these models have only considered luminance compression from a photographic point of view and have not explicitly taken into account colour appearance. Motivated by the idea to bridge the gap between crossmedia colour reproduction and HDR imaging, this thesis investigates the fundamentals and the infrastructure of cross-media colour reproduction. It restructures cross-media colour reproduction with respect to HDR imaging, and develops a novel cross-media colour reproduction system for HDR imaging. First, our HDR characterisation method enables us to measure HDR radiance values to a high accuracy that rivals spectroradiometers. Second, our colour appearance model enables us to predict human colour perception under high luminance levels. We first built a high-luminance display in order to establish a controllable high-luminance viewing environment. We conducted a psychophysical experiment on this display device to measure perceptual colour attributes. A novel numerical model for colour appearance was derived from our experimental data, which covers the full working range of the human visual system. Our appearance model predicts colour and luminance attributes under high luminance levels. In particular, our model predicts perceived lightness and colourfulness to a significantly higher accuracy than other appearance models. Finally, a complete colour reproduction pipeline is proposed using our novel HDR characterisation and colour appearance models. Results indicate that our reproduction system outperforms other reproduction methods with statistical significance. Our colour reproduction system provides high-fidelity colour reproduction for HDR imaging, and successfully bridges the gap between cross-media colour reproduction and HDR imaging

Guest editorial: high dynamic range imaging

High Dynamic Range (HDR) imagery is a step-change in imaging technology that is not limited to the 8-bits per pixel for each color channel that traditional or low-dynamic range digital images have been constrained to. These restrictions have meant that the current and relatively novel imaging technologies including stereoscopic, HD and ultraHD imaging do not provide an accurate representation of the lighting available in a real world environment. HDR technology has enabled the capture, storage, handling and display of content that supports real world luminance and facilitated the use of rendering methods in special effects, video games and advertising via novel rendering methods such as image-based lighting; it is also compatible with the other imaging methods and will certainly be a requirement of future high-fidelity imaging format specifications. However, HDR still has challenges to overcome before it can become a fully fledged commercially successful technology. This special issue goes someway in to rectify any limitations and also shines a light on future potential uses and directions of HDR

A new technique to reproduced high-dynamic-range images for low-dynamic-range display

Tone mapping is a process for reproduction of High-Dynamic-Range images (HDR) for Low-Dynamic-Range (LDR) output devices. In this report, author presents a new local tone-mapping operator, derived from the Contrast Limited Adaptive histogram Equalization (CLAHE) technique for displaying high dynamic range image. The CLAHE is a method which was originally developed for medical imaging. This method has effectively expanded the full dynamic range of display and it is fully automatic. Due to different luminance intervals could result in overlapped reaction on the limited response in limited response range of visual system, scene region splitting and merging were used to segment the scaled luminance and perform the image segmentation to segment image into smaller part. After the region splitting and merging, there will be some noise or variation of intensity that may result in holes or over segmentation. As the result, the morphological operation, opening and closing were used to perform the mask to applied different clip limit of the CLAHE operation

High Dynamic Range Adaptive Real-time Smart Camera: an overview of the HDR-ARTiST project

International audienceStandard cameras capture only a fraction of the information that is visible to the human visual system. This is specifically true for natural scenes including areas of low and high illumination due to transitions between sunlit and shaded areas. When capturing such a scene, many cameras are unable to store the full Dynamic Range (DR) resulting in low quality video where details are concealed in shadows or washed out by sunlight. The imaging technique that can overcome this problem is called HDR (High Dynamic Range) imaging. This paper describes a complete smart camera built around a standard off-the-shelf LDR (Low Dynamic Range) sensor and a Virtex-6 FPGA board. This smart camera called HDR-ARtiSt (High Dynamic Range Adaptive Real-time Smart camera) is able to produce a real-time HDR live video color stream by recording and combining multiple acquisitions of the same scene while varying the exposure time. This technique appears as one of the most appropriate and cheapest solution to enhance the dynamic range of real-life environments. HDR-ARtiSt embeds real-time multiple captures, HDR processing, data display and transfer of a HDR color video for a full sensor resolution (1280 1024 pixels) at 60 frames per second. The main contributions of this work are: (1) Multiple Exposure Control (MEC) dedicated to the smart image capture with alternating three exposure times that are dynamically evaluated from frame to frame, (2) Multi-streaming Memory Management Unit (MMMU) dedicated to the memory read/write operations of the three parallel video streams, corresponding to the different exposure times, (3) HRD creating by combining the video streams using a specific hardware version of the Devebecs technique, and (4) Global Tone Mapping (GTM) of the HDR scene for display on a standard LCD monitor

Live HDR video streaming on commodity hardware

High Dynamic Range (HDR) video provides a step change in viewing experience, for example the ability to clearly see the soccer ball when it is kicked from the shadow of the stadium into sunshine. To achieve the full potential of HDR video, so-called true HDR, it is crucial that all the dynamic range that was captured is delivered to the display device and tone mapping is confined only to the display. Furthermore, to ensure widespread uptake of HDR imaging, it should be low cost and available on commodity hardware. This paper describes an end-to-end HDR pipeline for capturing, encoding and streaming high-definition HDR video in real-time using off-the-shelf components. All the lighting that is captured by HDR-enabled consumer cameras is delivered via the pipeline to any display, including HDR displays and even mobile devices with minimum latency. The system thus provides an integrated HDR video pipeline that includes everything from capture to post-production, archival and storage, compression, transmission, and display

HDR video past, present and future : a perspective

High Dynamic Range (HDR) video has emerged from research labs around the world and entered the realm of consumer electronics. The dynamic range that a human can see in a scene with minimal eye adaption (approximately 1,000,000: 1) is vastly greater than traditional imaging technology which can only capture about 8 f-stops (256: 1). HDR technology, on the other hand, has the potential to capture the full range of light in a scene; even more than a human eye can see. This paper examines the field of HDR video from capture to display; past, present and future. In particular the paper looks beyond the current marketing hype around HDR, to show how HDR video in the future can and, indeed, should bring about a step change in imaging, analogous to the change from black and white to colour

Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression

Imaging systems are essential to a wide range of modern day applications. With the continuous advancement in imaging systems, there is an on-going need to adapt and improve the imaging pipeline running inside the imaging systems. In this thesis, methods are presented to improve the imaging pipeline of digital cameras. Here we present three methods to improve important phases of the imaging process, which are (i) ``Automatic exposure adjustment'' (ii) ``Radiometric calibration'' (iii) ''High dynamic range compression''. These contributions touch the initial, intermediate and final stages of imaging pipeline of digital cameras. For exposure control, we propose two methods. The first makes use of CCD-based equations to formulate the exposure control problem. To estimate the exposure time, an initial image was acquired for each wavelength channel to which contrast adjustment techniques were applied. This helps to recover a reference cumulative distribution function of image brightness at each channel. The second method proposed for automatic exposure control is an iterative method applicable for a broad range of imaging systems. It uses spectral sensitivity functions such as the photopic response functions for the generation of a spectral power image of the captured scene. A target image is then generated using the spectral power image by applying histogram equalization. The exposure time is hence calculated iteratively by minimizing the squared difference between target and the current spectral power image. Here we further analyze the method by performing its stability and controllability analysis using a state space representation used in control theory. The applicability of the proposed method for exposure time calculation was shown on real world scenes using cameras with varying architectures. Radiometric calibration is the estimate of the non-linear mapping of the input radiance map to the output brightness values. The radiometric mapping is represented by the camera response function with which the radiance map of the scene is estimated. Our radiometric calibration method employs an L1 cost function by taking advantage of Weisfeld optimization scheme. The proposed calibration works with multiple input images of the scene with varying exposure. It can also perform calibration using a single input with few constraints. The proposed method outperforms, quantitatively and qualitatively, various alternative methods found in the literature of radiometric calibration. Finally, to realistically represent the estimated radiance maps on low dynamic range display (LDR) devices, we propose a method for dynamic range compression. Radiance maps generally have higher dynamic range (HDR) as compared to the widely used display devices. Thus, for display purposes, dynamic range compression is required on HDR images. Our proposed method generates few LDR images from the HDR radiance map by clipping its values at different exposures. Using contrast information of each LDR image generated, the method uses an energy minimization approach to estimate the probability map of each LDR image. These probability maps are then used as label set to form final compressed dynamic range image for the display device. The results of our method were compared qualitatively and quantitatively with those produced by widely cited and professionally used methods