Showing papers on "High-dynamic-range imaging published in 2014"

Creating cinematic wide gamut HDR-video for the evaluation of tone mapping operators and HDR-displays

Abstract: High quality video sequences are required for the evaluation of tone mapping operators and high dynamic range (HDR) displays. We provide scenic and documentary scenes with a dynamic range of up to 18 stops. The scenes are staged using professional film lighting, make-up and set design to enable the evaluation of image and material appearance. To address challenges for HDR-displays and temporal tone mapping operators, the sequences include highlights entering and leaving the image, brightness changing over time, high contrast skin tones, specular highlights and bright, saturated colors. HDR-capture is carried out using two cameras mounted on a mirror-rig. To achieve a cinematic depth of field, digital motion picture cameras with Super-35mm size sensors are used. We provide HDR-video sequences to serve as a common ground for the evaluation of temporal tone mapping operators and HDR-displays. They are available to the scientific community for further research.

Ghost-Free High Dynamic Range Imaging via Rank Minimization

Abstract: We propose a ghost-free high dynamic range (HDR) image synthesis algorithm using a low-rank matrix completion framework, which we call RM-HDR. Based on the assumption that irradiance maps are linearly related to low dynamic range (LDR) image exposures, we formulate ghost region detection as a rank minimization problem. We incorporate constraints on moving objects, i.e., sparsity, connectivity, and priors on under- and over-exposed regions into the framework. Experiments on real image collections show that the RM-HDR can often provide significant gains in synthesized HDR image quality over state-of-the-art approaches. Additionally, a complexity analysis is performed which reveals computational merits of RM-HDR over recent advances in deghosting for HDR.

Systems and Methods for High Dynamic Range Imaging Using Array Cameras

Abstract: Systems and methods for high dynamic range imaging using array cameras in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, a method of generating a high dynamic range image using an array camera includes defining at least two subsets of active cameras, determining image capture settings for each subset of active cameras, where the image capture settings include at least two exposure settings, configuring the active cameras using the determined image capture settings for each subset, capturing image data using the active cameras, synthesizing an image for each of the at least two subset of active cameras using the captured image data, and generating a high dynamic range image using the synthesized images.

Hardware-based smart camera for recovering high dynamic range video from multiple exposures

Abstract: In many applications such as video surveillance or defect detection, the perception of information related to a scene is limited in areas with strong contrasts. The high dynamic range (HDR) capture technique can deal with these limitations. The proposed method has the advantage of automatically selecting multiple exposure times to make outputs more visible than fixed exposure ones. A real-time hardware implementation of the HDR technique that shows more details both in dark and bright areas of a scene is an important line of research. For this purpose, we built a dedicated smart camera that performs both capturing and HDR video processing from three exposures. What is new in our work is shown through the following points: HDR video capture through multiple exposure control, HDR memory management, HDR frame generation, and rep- resentation under a hardware context. Our camera achieves a real-time HDR video output at 60 fps at 1.3 mega- pixels and demonstrates the efficiency of our technique through an experimental result. Applications of this HDR smart camera include the movie industry, the mass-consumer market, military, automotive industry, and sur- veillance

Performance evaluation of objective quality metrics for HDR image compression

Abstract: Due to the much larger luminance and contrast characteristics of high dynamic range (HDR) images, well-known objective quality metrics, widely used for the assessment of low dynamic range (LDR) content, cannot be directly applied to HDR images in order to predict their perceptual fidelity. To overcome this limitation, advanced fidelity metrics, such as the HDR-VDP, have been proposed to accurately predict visually significant differences. However, their complex calibration may make them difficult to use in practice. A simpler approach consists in computing arithmetic or structural fidelity metrics, such as PSNR and SSIM, on perceptually encoded luminance values but the performance of quality prediction in this case has not been clearly studied. In this paper, we aim at providing a better comprehension of the limits and the potentialities of this approach, by means of a subjective study. We compare the performance of HDR-VDP to that of PSNR and SSIM computed on perceptually encoded luminance values, when considering compressed HDR images. Our results show that these simpler metrics can be effectively employed to assess image fidelity for applications such as HDR image compression.

Illumination systems and methods for computer imagers

Abstract: The present invention can provide solutions to many common imaging problems, such as, for example, unevenly distributed illumination, shadows, white balance adjustment, colored ambient light and high dynamic range imaging Imaging systems and methods can be provided through a computer (eg, laptop or desktop) such that the system or method can take advantage of the computer's processing power to provide functionality that goes beyond typical camera Such an imaging system may include an imaging device, a camera, a light source and a user interface

Motion Deblurring: Algorithms and Systems

Abstract: A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields.

Single shot high dynamic range imaging using piecewise linear estimators

Abstract: Building high dynamic range (HDR) images by combining photographs captured with different exposure times present several drawbacks, such as the need for global alignment and motion estimation in order to avoid ghosting artifacts. The concept of spatially varying pixel exposures (SVE) proposed by Nayar et al. enables to capture in only one shot a very large range of exposures while avoiding these limitations. In this paper, we propose a novel approach to generate HDR images from a single shot acquired with spatially varying pixel exposures. The proposed method makes use of the assumption stating that the distribution of patches in an image is well represented by a Gaussian Mixture Model. Drawing on a precise modeling of the camera acquisition noise, we extend the piecewise linear estimation strategy developed by Yu et al. for image restoration. The proposed method permits to reconstruct an irradiance image by simultaneously estimating saturated and under-exposed pixels and denoising existing ones, showing significant improvements over existing approaches.

Single-shot High Dynamic Range Imaging Using Coded Electronic Shutter

Abstract: Typical high dynamic range HDR imaging approaches based on multiple images have difficulties in handling moving objects and camera shakes, suffering from the ghosting effect and the loss of sharpness in the output HDR image. While there exist a variety of solutions for resolving such limitations, most of the existing algorithms are susceptible to complex motions, saturation, and occlusions. In this paper, we propose an HDR imaging approach using the coded electronic shutter which can capture a scene with row-wise varying exposures in a single image. Our approach enables a direct extension of the dynamic range of the captured image without using multiple images, by photometrically calibrating rows with different exposures. Due to the concurrent capture of multiple exposures, misalignments of moving objects are naturally avoided with significant reduction in the ghosting effect. To handle the issues with under-/over-exposure, noise, and blurs, we present a coherent HDR imaging process where the problems are resolved one by one at each step. Experimental results with real photographs, captured using a coded electronic shutter, demonstrate that our method produces a high quality HDR images without the ghosting and blur artifacts.

Subjective evaluation of higher dynamic range video

Abstract: High dynamic range (HDR) imaging is able to capture a wide range of luminance values, closer to what the human eye can perceive. However, for capture and display technologies, it is important to answer the question on the significance of higher dynamic range for user preference. This paper answers this question by investigating the added value of higher dynamic range via a rigorous set of subjective experiments using paired comparison methodology. Video sequences at four different peak luminance levels were displayed side-by-side on a Dolby Research HDR RGB backlight dual modulation display (aka `Pulsar'), which is capable of reliably displaying video content at 4000 cd/m2 peak luminance. The results of the subjective experiment demonstrate that the preference of an average viewer increases logarithmically with the increase in the maximum luminance level at which HDR content is displayed, with 4000 cd/m2 being the most attractive option.

An Assessment of High Dynamic Range Luminance Measurements with LED Lighting

Abstract: This study investigates whether high dynamic range imaging (HDRI) can accurately capture luminance of a single light emitting diode (LED) chip within the luminaire. Two conventional methods of determining luminance—the use of a luminance meter with a close-up lens and deriving luminance from illuminance measurements, source area, and distance—of a single LED chip are compared to HDRI measurements. The results show that HDRI using a Canon EOS 7D camera, fitted with 28–105 mm lens and a neutral density filter (with less than 1% transmittance) combined in Photosphere compares very well to a luminance value determined with goniophotometer measurements and calculations. It provides confidence in the ability of HDRI to capture luminance of a single LED chip.

Methods and apparatus for true high dynamic range imaging

Abstract: When imaging bright objects, a conventional detector array can saturate, making it difficult to produce an image with a dynamic range that equals the scene's dynamic range. Conversely, a digital focal plane array (DFPA) with one or more m-bit counters can produce an image whose dynamic range is greater than the native dynamic range. In one example, the DFPA acquires a first image over a relatively brief integration period at a relatively low gain setting. The DFPA then acquires a second image over longer integration period and/or a higher gain setting. During this second integration period, counters may roll over, possibly several times, to capture a residue modulus m of the number of counts (as opposed to the actual number of counts). A processor in or coupled to the DFPA generates a high-dynamic range image based on the first image and the residues modulus m.

Techniques for disparity estimation using camera arrays for high dynamic range imaging

Abstract: Techniques for improved focusing of camera arrays are described. In one embodiment, a system may include a processor circuit, a camera array, and an imaging management module for execution on the processor circuit to capture an array of images from the camera array, the array of images comprising first and second images taken with first and second values of an exposure parameter, respectively, the first value different than the second value, to estimate a noise level, to normalize an intensity of each image based upon the noise level of the respective image, to produce a respective normalized image, to identify candidate disparities in each of the respective normalized images, to estimate a high dynamic range (HDR) image patch for each candidate disparity, and to compute an error from the HDR image patch and an objective function, to produce a disparity estimate. Other embodiments are described and claimed.

Objective evaluation of naturalness, contrast, and colorfulness of tone-mapped images

Abstract: The main obstacle preventing High Dynamic Range (HDR) imaging from becoming standard in image and video processing industry is the challenge of displaying the content. The prices of HDR screens are still too high for ordinary customers. During last decade, a lot of effort has been dedicated to finding ways to compress the dynamic range for legacy displays with simultaneous preservation of details in highlights and shadows which cannot be achieved by standard systems. These dynamic range compression techniques are called tone-mapping operators (TMO) and introduce novel distortions such as spatially non-linear distortion of contrast or naturalness corruption. This paper provides an analysis of objective no-reference naturalness, contrast and colorfulness measures in the context of tone-mapped images evaluation. Reliable measures of these features could be further merged together into single overall quality metric. The main goal of the paper is to provide an initial study of the problem and identify the potential candidates for such a combination.

Photometric characterisation of small sources with high dynamic range illuminance mapping

Abstract: In this paper a system for obtaining the luminous intensity distribution of a small light source, based on high dynamic range imaging, is described. The system uses a dark room, a lambertian screen and a video-luminance-meter mounted on a workbench. The luminous intensity distribution of the light source is derived from the illuminance map on the screen by applying the photometric inverse law. The geometry of the system is presented and the measurements’ uncertainties are estimated. Finally, an application to a LED source is presented.

High dynamic range imaging systems

Abstract: A method for tone mapping a high dynamic range image to be presented on a display device (15). The display device is provided with a camera (17) which captures an image indicating ambient light levels experienced at a number of positions over the display (16) of the device. A data processing module (22) processes signals from the camera and generates data which is used in adapting a tone mapping function applied to the high dynamic range image so as to account for different ambient light conditions. The data processing module may take into account different lighting conditions at different positions on the display when generating the data which is used in adapting the tone mapping function. The data processing module may also use signals from a camera (20) on the other side of the device and / or from an orientation detection module (21). The data processing module may also use preference data supplied for a series of video frames, when adapting the tone mapping function. The device may be a mobile phone, a table computing device or a portable computer.

High Dynamic Range Imaging for Dynamic Scenes

Abstract: Although the currently available digital cameras typically provide 256 levels of brightness data at each pixel, a real world scenes contains a very wide range of brightness variations. High Dynamic Range (HDR) images contain wider range of brightness information than a normal image and are a new trend in photography. In the conventional method multiple photographs of same scene are captured at different exposures and then combined to produce HDR images. Clearly these methods require a still scene and are not able to produce HDR image for moving scene or moving camera. We present a method based on Spatial Varying Exposure (SVE) system. Here we capture only a single photograph of the scene but vary the exposure spatially. By this method it is possible to create the HDR image for dynamic scene by trading-off spatial resolution.

Frameless, time domain continuous image capture

Abstract: Most image sensors mimic film, integrating light during an exposure interval and then reading the "latent" image as a complete frame. In contrast, frameless image capture attempts to construct a continuous waveform for each sensel describing how the Ev (exposure value required at each sensel) changes over time. This is done using an array of on-sensor nanocontrollers, each independently and asynchronously sampling its sensel to interpolate a smooth waveform. Still images are computationally extracted after capture using the average value of each sensel’s waveform over the desired interval. Thus, image frames can be extracted to represent any interval(s) within the captured period. Because the extraction of a frame is done using waveforms that are continuous time-varying functions, an Ev estimate is always available, even if a particular sensel was not actually sampled during the desired interval. The result is HDR (high dynamic range) with a low and directly controllable Ev noise level. This paper describes our work toward building a frameless imaging sensor using nanocontrollers, basic processing of time domain continuous image data, and the expected benefits and problems.

High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging

Abstract: In this paper we emphasize a similarity between the Logarithmic-Type Image Processing (LTIP) model and the Naka-Rushton model of the Human Visual System (HVS). LTIP is a derivation of the Logarithmic Image Processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present an unifying framework for the High Dynamic Range (HDR) imaging problem, namely that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.

Spatially Adaptive Histogram Equalization for Single Image-Based Ghost-free High Dynamic Range Imaging

Abstract: In this paper, we present a spatially adaptive histogram equalization method for generating a ghost-free high dynamic range (HDR) image using a single input image. Existing multiple input-based HDR methods fuse multiple low dynamic range (LDR) images, acquired using different exposures. However, these methods work only under the assumption that neither global nor local motions exists in between the LDR images. To overcome such an unrealistic constraint, we generate two LDR images from a single input image. To generate LDR images with appropriate exposures, we divide the entire intensity range into multiple sub-ranges using histogram quantization and separately perform histogram equalization in each sub-range. Thus, we can acquire a set of differently exposed LDR images of the same scene, which are then fused to generate a ghost-free HDR image. The major contribution of this work is twofold: (i) a novel estimation method for providing optimal sub-ranges of intensity using histogram quantization, which (ii) requires no additional hardware for generating multiple LDR images. Because the proposed method uses a set of optimally self-generated LDR images, it is inherently free of ghost artifacts and can provide a ghost-free HDR function for low-cost, lightweight imaging devices, such as mobile phones and compact cameras.

Automatic face recognition in HDR imaging

Abstract: The gaining popularity of the new High Dynamic Range (HDR) imaging systems is raising new privacy issues caused by the methods used for visualization. HDR images require tone mapping methods for an appropriate visualization on conventional and non-expensive LDR displays. These visualization methods might result in completely different visualization raising several issues on privacy intrusion. In fact, some visualization methods result in a perceptual recognition of the individuals, while others do not even show any identity. Although perceptual recognition might be possible, a natural question that can rise is how computer based recognition will perform using tone mapping generated images? In this paper, a study where automatic face recognition using sparse representation is tested with images that result from common tone mapping operators applied to HDR images. Its ability for the face identity recognition is described. Furthermore, typical LDR images are used for the face recognition training.

On improving the pooling in HDR-VDP-2 towards better HDR perceptual quality assessment

Abstract: High Dynamic Range (HDR) signals capture much higher contrasts as compared to the traditional 8-bit low dynamic range (LDR) signals. This is achieved by representing the visual signal via values that are related to the real-world luminance, instead of gamma encoded pixel values which is the case with LDR. Therefore, HDR signals cover a larger luminance range and tend to have more visual appeal. However, due to the higher luminance conditions, the existing methods cannot be directly employed for objective quality assessment of HDR signals. For that reason, the HDR Visual Difference Predictor (HDR-VDP-2) has been proposed. HDR-VDP-2 is primarily a visibility prediction metric i.e. whether the signal distortion is visible to the eye and to what extent. Nevertheless, it also employs a pooling function to compute an overall quality score. This paper focuses on the pooling aspect in HDR-VDP-2 and employs a comprehensive database of HDR images (with their corresponding subjective ratings) to improve the prediction accuracy of HDR-VDP-2. We also discuss and evaluate the existing objective methods and provide a perspective towards better HDR quality assessment.

Prediction-guided quantization for video tone mapping

Abstract: Tone Mapping Operators (TMOs) compress High Dynamic Range (HDR) content to address Low Dynamic Range (LDR) displays. However, before reaching the end-user, this tone mapped content is usually compressed for broadcasting or storage purposes. Any TMO includes a quantization step to convert floating point values to integer ones. In this work, we propose to adapt this quantization, in the loop of an encoder, to reduce the entropy of the tone mapped video content. Our technique provides an appropriate quantization for each mode of both the Intra and Inter-prediction that is performed in the loop of a block-based encoder. The mode that minimizes a rate-distortion criterion uses its associated quantization to provide integer values for the rest of the encoding process. The method has been implemented in HEVC and was tested over two different scenarios: the compression of tone mapped LDR video content (using the HM10.0) and the compression of perceptually encoded HDR content (HM14.0). Results show an average bit-rate reduction under the same PSNR for all the sequences and TMO considered of 20.3% and 27.3% for tone mapped content and 2.4% and 2.7% for HDR content.

Multiple-samples-method enabling high dynamic range imaging for high frame rate CMOS image sensor by FPGA and co-processor

Abstract: We present results from a prototype CMOS camera system implementing a multiple sampled pixel level algorithm (“Last Sample Before Saturation”) to create High-Dynamic Range (HDR) images that approach the dynamic range of CCDs. The system is built around a commercial 1280 × 1024 CMOS image sensor with 10-bits per pixel and up to 500 Hz full frame rate with higher frame rates available through windowing. We analyze imagery data collected at room temperature for SNR versus photocurrent, among other figures of merit. Results conform to expectations of a model that uses only dark current, read noise, and photocurrent as input parameters.

An automated approach for tone mapping operator parameter adjustment in security applications

Abstract: High Dynamic Range (HDR) imaging has been gaining popularity in recent years. Different from the traditional low dynamic range (LDR), HDR content tends to be visually more appealing and realistic as it can represent the dynamic range of the visual stimuli present in the real world. As a result, more scene details can be faithfully reproduced. As a direct consequence, the visual quality tends to improve. HDR can be also directly exploited for new applications such as video surveillance and other security tasks. Since more scene details are available in HDR, it can help in identifying/tracking visual information which otherwise might be difficult with typical LDR content due to factors such as lack/excess of illumination, extreme contrast in the scene, etc. On the other hand, with HDR, there might be issues related to increased privacy intrusion. To display the HDR content on the regular screen, tone-mapping operators (TMO) are used. In this paper, we present the universal method for TMO parameters tuning, in order to maintain as many details as possible, which is desirable in security applications. The method’s performance is verified on several TMOs by comparing the outcomes from tone-mapping with default and optimized parameters. The results suggest that the proposed approach preserves more information which could be of advantage for security surveillance but, on the other hand, makes us consider possible increase in privacy intrusion.

Ghost and noise removal in exposure fusion for high dynamic range imaging

Abstract: For producing a single high dynamic range image (HDRI), multiple low dynamic range images (LDRIs) are captured with different exposures and combined. In high dynamic range (HDR) imaging, local motion of objects and noise in a set of LDRIs can influence a final HDRI: local motion of objects causes the ghost artifact and LDRIs, especially captured with under-exposure, make the final HDRI noisy. In this paper, we propose a ghost and noise removal method for HDRI using exposure fusion with subband architecture, in which Haar wavelet filter is used. The proposed method blends weight map of exposure fusion in the subband pyramid, where the weight map is produced for ghost artifact removal as well as exposure fusion. Then, the noise is removed using multi-resolution bilateral filtering. After removing the ghost artifact and noise in subband images, details of the images are enhanced using a gain control map. Experimental results with various sets of LDRIs show that the proposed method effectively removes the ghost artifact and noise, enhancing the contrast in a final HDRI.

System and method of fast adaptive blending for high dynamic range imaging

Abstract: A system for high dynamic range (HDR) imaging and methods for making and using same is disclosed. An HDR module in a camera initializes a set of lookup tables (LUTs) in YUV color space based on exposure configurations of a set of images taken as HDR imaging. The HDR module calculates weights of luminance Y components of the set of images in YUV color space. Based on the calculated weights, the HDR module blends the Y component of the set of images to generate blended Y components. The HDR module combines the blended Y components with corresponding UV components to generate a single image in YUV space. Thereby, the HDR module advantageously combines a set of images into a blended HDR image with only blending the Y component of the set of images.

Gamut mapping in a high-dynamic-range color space

Abstract: In this paper, we present a novel approach of tone mapping as gamut mapping in a high-dynamic-range (HDR) color space. High- and low-dynamic-range (LDR) images as well as device gamut boundaries can simultaneously be represented within such a color space. This enables a unified transformation of the HDR image into the gamut of an output device (in this paper called HDR gamut mapping). An additional aim of this paper is to investigate the suitability of a specific HDR color space to serve as a working color space for the proposed HDR gamut mapping. For the HDR gamut mapping, we use a recent approach that iteratively minimizes an image-difference metric subject to in-gamut images. A psychophysical experiment on an HDR display shows that the standard reproduction workflow of two subsequent transformations – tone mapping and then gamut mapping – may be improved by HDR gamut mapping.

A detail enhancement and dynamic range adjustment algorithm for high dynamic range images

Abstract: Although high dynamic range (HDR) images contain large amounts of information, they have weak texture and low contrast. What's more, these images are difficult to be reproduced on low dynamic range displaying mediums. If much more information is to be acquired when these images are displayed on PCs, some specific transforms, such as compressing the dynamic range, enhancing the portions of little difference in original contrast and highlighting the texture details on the premise of keeping the parts of large contrast, are needed. To this ends, a multi-scale guided filter enhancement algorithm which derives from the single-scale guided filter based on the analysis of non-physical model is proposed in this paper. Firstly, this algorithm decomposes the original HDR images into base image and detail images of different scales, and then it adaptively selects a transform function which acts on the enhanced detail images and original images. By comparing the treatment effects of HDR images and low dynamic range (LDR) images of different scene features, it proves that this algorithm, on the basis of maintaining the hierarchy and texture details of images, not only improves the contrast and enhances the details of images, but also adjusts the dynamic range well. Thus, it is much suitable for human observation or analytical processing of machines.