Motion Aware Exposure Bracketing for HDR Video

Mobile phones and tablets are rapidly gaining significance as omnipresent image and video capture devices. In this context we present an algorithm that allows such devices to capture high dynamic range (HDR) video. The design of the algorithm was informed by a perceptual study that assesses the relative importance of motion and dynamic range. We found that ghosting artefacts are more visually disturbing than a reduction in dynamic range, even if a comparable number of pixels is affected by each. We incorporated these findings into a real‐time, adaptive metering algorithm that seamlessly adjusts its settings to take exposures that will lead to minimal visual artefacts after recombination into an HDR sequence. It is uniquely suitable for real‐time selection of exposure settings. Finally, we present an off‐line HDR reconstruction algorithm that is matched to the adaptive nature of our real‐time metering approach.     

Convolutional Sparse Coding for High Dynamic Range Imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially‐varying pixel exposures. In this paper, we propose a novel algorithm to recover high‐quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently‐introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher‐quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.     

Freehand HDR Imaging of Moving Scenes with Simultaneous Resolution Enhancement

Despite their high popularity, common high dynamic range (HDR) methods are still limited in their practical applicability: They assume that the input images are perfectly aligned, which is often violated in practise. Our paper does not only free the user from this unrealistic limitation, but even turns the missing alignment into an advantage: By exploiting the multiple exposures, we can create a super‐resolution image. The alignment step is performed by a modern energy‐based optic flow approach that takes into account the varying exposure conditions. Moreover, it produces dense displacement fields with subpixel precision. As a consequence, our approach can handle arbitrary complex motion patterns, caused by severe camera shake and moving objects. Additionally, it benefits from several advantages over existing strategies: (i) It is robust under outliers (noise, occlusions, saturation problems) and allows for sharp discontinuities in the displacement field. (ii) The alignment step neither requires camera calibration nor knowledge of the exposure times. (iii) It can be efficiently implemented on CPU and GPU architectures. After the alignment is performed, we use the obtained subpixel accurate displacement fields as input for an energy‐based, joint super‐resolution and HDR (SR‐HDR) approach. It introduces robust data terms and anisotropic smoothness terms in the SR‐HDR literature. Our experiments with challenging real world data demonstrate that these novelties are pivotal for the favourable performance of our approach.     

The State of the Art in HDR Deghosting: A Survey and Evaluation

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and object movement has been a long‐standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: (1) how to detect ghost regions and (2) what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under‐ and over‐exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state‐of‐the‐art deghosting algorithms.     

Motion Deblurring from a Single Image using Circular Sensor Motion

Image blur caused by object motion attenuates high frequency content of images, making post‐capture deblurring an ill‐posed problem. The recoverable frequency band quickly becomes narrower for faster object motion as high frequencies are severely attenuated and virtually lost. This paper proposes to translate a camera sensor circularly about the optical axis during exposure, so that high frequencies can be preserved for a wide range of in‐plane linear object motion in any direction within some predetermined speed. That is, although no object may be photographed sharply at capture time, differently moving objects captured in a single image can be deconvolved with similar quality. In addition, circular sensor motion is shown to facilitate blur estimation thanks to distinct frequency zero patterns of the resulting motion blur point‐spread functions. An analysis of the frequency characteristics of circular sensor motion in relation to linear object motion is presented, along with deconvolution results for photographs captured with a prototype camera.     

Reinterpretable Imager: Towards Variable Post‐Capture Space,Angle and Time Resolution in Photography

We describe a novel multiplexing approach to achieve tradeoffs in space, angle and time resolution in photography. We explore the problem of mapping useful subsets of time‐varying 4D lightfields in a single snapshot. Our design is based on using a dynamic mask in the aperture and a static mask close to the sensor. The key idea is to exploit scene‐specific redundancy along spatial, angular and temporal dimensions and to provide a programmable or variable resolution tradeoff among these dimensions. This allows a user to reinterpret the single captured photo as either a high spatial resolution image, a refocusable image stack or a video for different parts of the scene in post‐processing. A lightfield camera or a video camera forces a‐priori choice in space‐angle‐time resolution. We demonstrate a single prototype which provides flexible post‐capture abilities not possible using either a single‐shot lightfield camera or a multi‐frame video camera. We show several novel results including digital refocusing on objects moving in depth and capturing multiple facial expressions in a single photo.     

Multisampling Compressive Video Spectroscopy

The coded aperture snapshot spectral imaging (CASSI) architecture has been employed widely for capturing hyperspectral video. Despite allowing concurrent capture of hyperspectral video, spatial modulation in CASSI sacrifices image resolution significantly while reconstructing spectral projection via sparse sampling. Several multiview alternatives have been proposed to handle this low spatial resolution problem and improve measurement accuracy, for instance, by adding a translation stage for the coded aperture or changing the static coded aperture with a digital micromirror device for dynamic modulation. State‐of‐the‐art solutions enhance spatial resolution significantly but are incapable of capturing video using CASSI. In this paper, we present a novel compressive coded aperture imaging design that increases spatial resolution while capturing 4D hyperspectral video of dynamic scenes. We revise the traditional CASSI design to allow for multiple sampling of the randomness of spatial modulation in a single frame. We demonstrate that our compressive video spectroscopy approach yields enhanced spatial resolution and consistent measurements, compared with the traditional CASSI design.     

Coded exposure HDR light‐field video recording

Capturing exposure sequences to compute high dynamic range (HDR) images causes motion blur in cases of camera movement. This also applies to light‐field cameras: frames rendered from multiple blurred HDR light‐field perspectives are also blurred. While the recording times of exposure sequences cannot be reduced for a single‐sensor camera, we demonstrate how this can be achieved for a camera array. Thus, we decrease capturing time and reduce motion blur for HDR light‐field video recording. Applying a spatio‐temporal exposure pattern while capturing frames with a camera array reduces the overall recording time and enables the estimation of camera movement within one light‐field video frame. By estimating depth maps and local point spread functions (PSFs) from multiple perspectives with the same exposure, regional motion deblurring can be supported. Missing exposures at various perspectives are then interpolated.     

From Faces to Outdoor Light Probes

Image‐based lighting has allowed the creation of photo‐realistic computer‐generated content. However, it requires the accurate capture of the illumination conditions, a task neither easy nor intuitive, especially to the average digital photography enthusiast. This paper presents an approach to directly estimate an HDR light probe from a single LDR photograph, shot outdoors with a consumer camera, without specialized calibration targets or equipment. Our insight is to use a person's face as an outdoor light probe. To estimate HDR light probes from LDR faces we use an inverse rendering approach which employs data‐driven priors to guide the estimation of realistic, HDR lighting. We build compact, realistic representations of outdoor lighting both parametrically and in a data‐driven way, by training a deep convolutional autoencoder on a large dataset of HDR sky environment maps. Our approach can recover high‐frequency, extremely high dynamic range lighting environments. For quantitative evaluation of lighting estimation accuracy and relighting accuracy, we also contribute a new database of face photographs with corresponding HDR light probes. We show that relighting objects with HDR light probes estimated by our method yields realistic results in a wide variety of settings.     

Exposure Fusion for Time‐Of‐Flight Imaging

This work deals with the problem of automatically choosing the correct exposure (or integration) time for time‐of‐flight depth image capturing. We apply methods known from high dynamic range imaging to combine depth images taken with differing integration times in order to produce high quality depth maps. We evaluate the quality of these depth maps by comparing the performance in reconstruction of planar textured patches and in the 3D reconstruction of an indoor scene. Our solution is fast enough to capture the images at interactive frame rates and also flexible to deal with any amount of exposures.     

An Objective Deghosting Quality Metric for HDR Images

Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.     

Outdoor Human Motion Capture by Simultaneous Optimization of Pose and Camera Parameters

We present a method for capturing the skeletal motions of humans using a sparse set of potentially moving cameras in an uncontrolled environment. Our approach is able to track multiple people even in front of cluttered and non‐static backgrounds, and unsynchronized cameras with varying image quality and frame rate. We completely rely on optical information and do not make use of additional sensor information (e.g. depth images or inertial sensors). Our algorithm simultaneously reconstructs the skeletal pose parameters of multiple performers and the motion of each camera. This is facilitated by a new energy functional that captures the alignment of the model and the camera positions with the input videos in an analytic way. The approach can be adopted in many practical applications to replace the complex and expensive motion capture studios with few consumer‐grade cameras even in uncontrolled outdoor scenes. We demonstrate this based on challenging multi‐view video sequences that are captured with unsynchronized and moving (e.g. mobile‐phone or GoPro) cameras.     

Evaluation of Tone Mapping Operators for HDR‐Video

Eleven tone‐mapping operators intended for video processing are analyzed and evaluated with camera‐captured and computer‐generated high‐dynamic‐range content. After optimizing the parameters of the operators in a formal experiment, we inspect and rate the artifacts (flickering, ghosting, temporal color consistency) and color rendition problems (brightness, contrast and color saturation) they produce. This allows us to identify major problems and challenges that video tone‐mapping needs to address. Then, we compare the tone‐mapping results in a pair‐wise comparison experiment to identify the operators that, on average, can be expected to perform better than the others and to assess the magnitude of differences between the best performing operators.     

Image Synthesis for Branching Structures

We present a set of techniques for the synthesis of artificial images that depict branching structures like rivers, cracks, lightning, mountain ranges, or blood vessels. The central idea is to build a statistical model that captures the characteristic bending and branching structure from example images. Then a new skeleton structure is synthesized and the final output image is composed from image fragments of the original input images. The synthesis part of our algorithm runs mostly automatic but it optionally allows the user to control the process in order to achieve a specific result. The combination of the statistical bending and branching model with sophisticated fragment‐based image synthesis corresponds to a multi‐resolution decomposition of the underlying branching structure into the low frequency behavior (captured by the statistical model) and the high frequency detail (captured by the image detail in the fragments). This approach allows for the synthesis of realistic branching structures, while at the same time preserving important textural details from the original image.     

Free Form Incident Light Fields

This paper presents methods for photo‐realistic rendering using strongly spatially variant illumination captured from real scenes. The illumination is captured along arbitrary paths in space using a high dynamic range, HDR, video camera system with position tracking. Light samples are rearranged into 4‐D incident light fields (ILF) suitable for direct use as illumination in renderings. Analysis of the captured data allows for estimation of the shape, position and spatial and angular properties of light sources in the scene. The estimated light sources can be extracted from the large 4D data set and handled separately to render scenes more efficiently and with higher quality. The ILF lighting can also be edited for detailed artistic control.     

Light montage for perceptual image enhancement

Recent photography techniques such as sculpting with light show great potential in compositing beautiful images from fixed‐viewpoint photos under multiple illuminations. The process relies heavily on the artists’ experience and skills using the available tools. An apparent trend in recent works is to facilitate the interaction making it less time‐consuming and addressable not only to experts, but also novices. We propose a method that automatically creates enhanced light montages that are comparable to those produced by artists. It detects and emphasizes cues that are important for perception by introducing a technique to extract depth and shape edges from an unconstrained light stack. Studies show that these cues are associated with silhouettes and suggestive contours which artists use to sketch and construct the layout of paintings. Textures, due to perspective distortion, offer essential cues that depict shape and surface slant. We balance the emphasis between depth edges and reflectance textures to enhance the sense of both shape and reflectance properties. Our light montage technique works perfectly with a few to hundreds of illuminations for each scene. Experiments show great results for static scenes making it practical for small objects, interiors and small‐scale outdoor scenes. Dynamic scenes may be captured using spatially distributed light setups such as light domes. The approach could also be applied to time‐lapse photos, with the sun as the main light source.     

Spectralization: Reconstructing spectra from sparse data

Traditional RGB reflectance and light data suffers from the problem of metamerism and is not suitable for rendering purposes where exact color reproduction under many different lighting conditions is needed. Nowadays many setups for cheap and fast acquisition of RGB or similar trichromatic datasets are available. In contrast to this, multi‐ or even hyper‐spectral measurements require costly hardware and have severe limitations in many cases. In this paper, we present an approach to combine efficiently captured RGB data with spectral data that can be captured with small additional effort for example by scanning a single line of an image using a spectral line‐scanner. Our algorithm can infer spectral reflectances and illumination from such sparse spectral and dense RGB data. Unlike other approaches, our method reaches acceptable perceptual errors with only three channels for the dense data and thus enables further use of highly efficient RGB capture systems. This way, we are able to provide an easier and cheaper way to capture spectral textures, BRDFs and environment maps for the use in spectral rendering systems.     

Characterization for High Dynamic Range Imaging

In this paper we present a new practical camera characterization technique to improve color accuracy in high dynamic range (HDR) imaging. Camera characterization refers to the process of mapping device‐dependent signals, such as digital camera RAW images, into a well‐defined color space. This is a well‐understood process for low dynamic range (LDR) imaging and is part of most digital cameras — usually mapping from the raw camera signal to the sRGB or Adobe RGB color space. This paper presents an efficient and accurate characterization method for high dynamic range imaging that extends previous methods originally designed for LDR imaging. We demonstrate that our characterization method is very accurate even in unknown illumination conditions, effectively turning a digital camera into a measurement device that measures physically accurate radiance values — both in terms of luminance and color — rivaling more expensive measurement instruments.     

Evaluation of Tone‐Mapping Operators for HDR Video Under Different Ambient Luminance Levels

Since high dynamic range (HDR) displays are not yet widely available, there is still a need to perform a dynamic range reduction of HDR content to reproduce it properly on standard dynamic range (SDR) displays. The most common techniques for performing this reduction are termed tone‐mapping operators (TMOs). Although mobile devices are becoming widespread, methods for displaying HDR content on these SDR screens are still very much in their infancy. While several studies have been conducted to evaluate TMOs, few have been done with a goal of testing small screen displays (SSDs), common on mobile devices. This paper presents an evaluation of six state‐of‐the‐art HDR video TMOs. The experiments considered three different levels of ambient luminance under which 180 participants were asked to rank the TMOs for seven tone‐mapped HDR video sequences. A comparison was conducted between tone‐mapped HDR video footage shown on an SSD and on a large screen SDR display using an HDR display as reference. The results show that there are differences between the performance of the TMOs under different ambient lighting levels and the TMOs that perform well on traditional large screen displays also perform well on SSDs at the same given luminance level.