Revertible tone mapping of high dynamic range imagery: Integration to JPEG 2000

This paper presents a revertible tone mapping approach based on subband architecture where the dynamic range of the HDR (High Dynamic Range) image is decreased to LDR (Low Dynamic Range) to fit several types of applications. The LDR image can be later expanded to get back the original HDR content. One important benefit of the proposed approach is its backward compatibility with low dynamic (LDR) image applications since no extra information is needed to perform a very efficient HDR reconstruction. In order to improve the efficiency of our TM (Tone Mapping), we couple it with an optimisation procedure to minimize the reconstruction error. Subjective and objective comparisons with state-of-the-art methods have shown superior quality results of both tone mapped and reconstructed images. As a potential application, the integration of the proposed tone mapping to JPEG 2000 encoder achieved competitive performance compared to reference HDR image encoders.     

Physiological inverse tone mapping based on retina response

The mismatch between the Low Dynamic Range (LDR) content and the High Dynamic Range (HDR) display arouses the research on inverse tone mapping algorithms. In this paper, we present a physiological inverse tone mapping algorithm inspired by the property of the Human Visual System (HVS). It first imitates the retina response and deduce it to be local adaptive; then estimates local adaptation luminance at each point in the image; finally, the LDR image and local luminance are applied to the inversed local retina response to reconstruct the dynamic range of the original scene. The good performance and high-visual quality were validated by operating on 40 test images. Comparison results with several existing inverse tone mapping methods prove the conciseness and efficiency of the proposed algorithm.     

Reconstruction of high contrast images for dynamic scenes

High Dynamic Range (HDR) imaging requires one to composite multiple, differently exposed images of a scene in the irradiance domain and perform tone mapping of the generated HDR image for displaying on Low Dynamic Range (LDR) devices. In the case of dynamic scenes, standard techniques may introduce artifacts called ghosts if the scene changes are not accounted for. In this paper, we consider the blind HDR problem for dynamic scenes. We develop a novel bottom-up segmentation algorithm through superpixel grouping which enables us to detect scene changes. We then employ a piecewise patch-based compositing methodology in the gradient domain to directly generate the ghost-free LDR image of the dynamic scene. Being a blind method, the primary advantage of our approach is that we do not assume any knowledge of camera response function and exposure settings while preserving the contrast even in the non-stationary regions of the scene. We compare the results of our approach for both static and dynamic scenes with that of the state-of-the-art techniques.     

基于多尺度分解的色调映射算法

针对高动态范围(HDR)图像显示于普通显示设备的问题,提出一种新的基于多尺度分解的色调映射(TM)算法。首先利用局部边缘保留(LEP)滤波器对HDR图像进行多尺度分解,有效平滑了图像的细节同时保留了突出的边缘;根据分解后各层的特点和压缩的要求,提出一个带参数的动态范围压缩函数,通过变化参数以便压缩图像的粗尺度层并增强细尺度层,从而压缩图像的动态范围并增强细节;最后重组各层并恢复颜色,所得到的映射后图像具有良好的视觉效果。实验结果证明,该方法在自然度、结构保真度和整体的质量评价上都要优于Gu等(GU B, LI W J, ZHU M Y, et al. Local edge-preserving multiscale decomposition for high dynamic range image tone mapping ［J］. IEEE Transactions on Image Processing, 2013, 22(1): 70-79)和Yeganeh等(YEGANEH H, WANG Z. Objective quality assessment of tone-mapped images ［J］. IEEE Transactions on Image Processing, 2013, 22(2): 657-667)提出的方法,同时也避免了局部色调映射算法所普遍存在的光晕效应。该算法可以用于HDR图像的色调映射。     

Attention-based high dynamic range imaging

Many tone mapping algorithms have been proposed based on the studies in Human Visual System; however, they rarely addressed the effects of attention to contrast response. As attention plays an important role in human visual system, we proposed a local tone mapping method that respects both attention and adaptation effects. We adopt the High Dynamic Range (HDR) saliency map to compute an attention map, which predicts the attentive regions and nonattentative regions in an HDR image. The attention map is then used to locally adjust the contrast of the HDR image according to attention and adaptation models found in psychophysics. We applied our tone mapping approach to HDR images and videos and compared with the results generated by three state-of-the-art tone mapping algorithms. Our experiments show that our approach produces results with better image quality in terms of preserving details and chromaticity of visual saliency.     

A high dynamic range rendering pipeline for interactive applications

Realistic images can be computed at interactive frame rates for Computer Graphics applications. Meanwhile, High Dynamic Range (HDR) rendering has a growing success in video games and virtual reality applications, as it improves the image quality and the player’s immersion feeling. In this paper, we propose a new method, based on a physical lighting model, to compute in real time a HDR illumination in virtual environments. Our method allows to re-use existing virtual environments as input, and computes HDR images in photometric units. Then, from these HDR images, displayable 8-bit images are rendered with a tone mapping operator and displayed on a standard display device. The HDR computation and the tone mapping are implemented in OpenSceneGraph with pixel shaders. The lighting model, together with a perceptual tone mapping, improves the perceptual realism of the rendered images at low cost. The method is illustrated with a practical application where the dynamic range of the virtual environment is a key rendering issue: night-time driving simulation.     

A Psychophysical Evaluation of Inverse Tone Mapping Techniques

In recent years inverse tone mapping techniques have been proposed for enhancing low-dynamic range (LDR) content for a high-dynamic range (HDR) experience on HDR displays, and for image based lighting. In this paper, we present a psychophysical study to evaluate the performance of inverse (reverse) tone mapping algorithms. Some of these techniques are computationally expensive because they need to resolve quantization problems that can occur when expanding an LDR image. Even if they can be implemented efficiently on hardware, the computational cost can still be high. An alternative is to utilize less complex operators; although these may suffer in terms of accuracy. Our study investigates, firstly, if a high level of complexity is needed for inverse tone mapping and, secondly, if a correlation exists between image content and quality. Two main applications have been considered: visualization on an HDR monitor and image-based lighting.     

一种亮度可控与细节保持的高动态范围图像色调映射方法

高动态范围(High dynamic range, HDR)图像通常需压缩其动态范围,以便于进行存储、传输、重现. 本文提出一种具有亮度可控与细节保持特性的HDR图像的全局色调映射方法.该方法对HDR图像 照度直方图进行裁剪与补偿,令色调映射后的低动态范围(Low dynamic range, LDR)图像仍能够保持原有的细节特性, 同时利用概率模型估算出输出LDR图像的亮度与标准差,进而调整直方图亮度区域的分配, 使得输出LDR图像的亮度接近用户设置的亮度,最后以分段直方图均衡的方法进行HDR色调映射处理. 仿真结果表明,该方法能对HDR图像动态范围进行合理的压缩映射,输出的LDR图像的亮度可由用户控制或自适应选择, 同时能保持图像的细节信息,令图像的主观视觉感受对比和谐.     

ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content

High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end‐to‐end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.     

High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image‐Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re‐use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.     

HDR到LDR图像的分段式对数映射算法

为了适应低端显示设备输出,需要一定的方法将高动态范围（HDR)图像转换为相应的低动态范围（LDR)图像。如果既考虑到人眼对亮度反应呈对数变化又利用图像自身的亮度分布,对高动态范围图像进行先全局后局部的映射,便得到一种分段式对数映射算法。该算法的复杂度较低,在视觉效果上结合了对数映射和分段映射算法的特点。     

Tone mapping to minimize visual sensation distortion considering brightness and local band‐limited contrast

We propose a tone‐mapping algorithm that minimizes the function that represents the visual sensation distortion that occurs after tone mapping. For the function, we consider both brightness and local band‐limited contrast to reduce darkening of images that have high dynamic range when they are displayed with conventional contrast‐based tone mapping using histogram on devices that have low dynamic range. By exploiting human visual characteristics, we simplify the problem and find a closed‐form solution that minimizes the function that represents distortion of visual sensation. In both subjective and objective evaluations, the proposed algorithm achieves a processed image that is most similar to the original image and has the best subjective image quality.     

一种基于细节层分离的单曝光HDR图像生成算法

针对利用单幅低动态范围（Low dynamic range,LDR）图像生成高动态范围（High dynamic range,HDR）图像细节信息不足的问题,本文提出了一种基于细节层分离的单曝光HDR图像生成算法.该算法基于人类视觉系统模型,首先分别提取出LDR图像的亮度分量和色度分量,对伽马校正后的亮度分量进行双边滤波,提取出亮度分量的基本层,再对基本层和亮度分量进行遍历运算,得到亮度分量的细节层;然后,构造反色调映射函数,分别对细节层和伽马校正后的亮度图像进行扩展,得到各自的反色调映图像;之后,将反色调映射后亮度分量与压缩后的细节层进行融合,得到新的亮度分量.最后,融合色度分量与新的亮度分量,并对融合后图像进行去噪,得到最终的HDR图像.实验表明该算法能挖掘出部分隐藏的图像细节信息,处理效果较好,运行效率高,具有较好的鲁棒性.     

High dynamic range image tone mapping and retexturing using fast trilateral filtering

Using fast trilateral filtering we present a novel tone mapping and retexturing method for high dynamic range (HDR) images. Our new trilateral filtering-based tone mapping is about seven to ten times faster than that in [3]. Firstly, a novel tone mapping algorithm for HDR images is presented. It is based on fast bilateral filtering and two newly developed filters: the quasi-Cauchy function kernel filter and the fourth degree Taylor polynomial kernel filter. Secondly, a new gradient-based image retexturing method is introduced, which consists of three steps: 1) converting HDR images into low dynamic range (LDR) images using our fast trilateral filtering-based tone mapping method; 2) recovering the gradient luminance maps for the region to be retextured; 3) reconstructing the final retextured image by solving the Poisson equation. The proposed approach is suitable for HDR image tone mapping and retexturing, and experimental results have demonstrated the satisfactory performance of our method.     

Application‐Specific Tone Mapping Via Genetic Programming

High dynamic range (HDR) imagery permits the manipulation of real‐world data distinct from the limitations of the traditional, low dynamic range (LDR), content. The process of retargeting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualizing HDR content on traditional displays, supporting backwards‐compatible HDR compression and, more recently, is being frequently used for input into a wide variety of computer vision applications. This work presents the automatic generation of TMOs for specific applications via the evolutionary computing method of genetic programming (GP). A straightforward, generic GP method that generates TMOs for a given fitness function and HDR content is presented. Its efficacy is demonstrated in the context of three applications: Visualization of HDR content on LDR displays, feature mapping and compression. For these applications, results show good performance for the generated TMOs when compared to traditional methods. Furthermore, they demonstrate that the method is generalizable and could be used across various applications that require TMOs but for which dedicated successful TMOs have not yet been discovered.     

Temporal Glare: Real-Time Dynamic Simulation of the Scattering in the Human Eye

Glare is a consequence of light scattered within the human eye when looking at bright light sources. This effect can be exploited for tone mapping since adding glare to the depiction of high-dynamic range (HDR) imagery on a low-dynamic range (LDR) medium can dramatically increase perceived contrast. Even though most, if not all, subjects report perceiving glare as a bright pattern that fluctuates in time, up to now it has only been modeled as a static phenomenon. We argue that the temporal properties of glare are a strong means to increase perceived brightness and to produce realistic and attractive renderings of bright light sources. Based on the anatomy of the human eye, we propose a model that enables real-time simulation of dynamic glare on a GPU. This allows an improved depiction of HDR images on LDR media for interactive applications like games, feature films, or even by adding movement to initially static HDR images. By conducting psychophysical studies, we validate that our method improves perceived brightness and that dynamic glare-renderings are often perceived as more attractive depending on the chosen scene.     

Robust artifact-free high dynamic range imaging of dynamic scenes

The irradiance range of the real-world scene is often beyond the capability of digital cameras. Therefore, High Dynamic Range (HDR) images can be generated by fusing images with different exposure of the same scene. However, moving objects pose the most severe problem in the HDR imaging, leading to the annoying ghost artifacts in the fused image. In this paper, we present a novel HDR technique to address the moving objects problem. Since the input low dynamic range (LDR) images captured by a camera act as static linear related backgrounds with moving objects during each individual exposures, we formulate the detection of foreground moving objects as a rank minimization problem. Meanwhile, in order to eliminate the image blurring caused by background slightly change of LDR images, we further rectify the background by employing the irradiances alignment. Experiments on image sequences show that the proposed algorithm performs significant gains in synthesized HDR image quality compare to state-of-the-art methods.

     

基于亮度分区模糊融合的高动态范围成像算法

针对单幅图像生成高动态范围（HDR）图像进行直方图扩展时,造成的色彩失真、局部细节信息丢失的问题,提出了一种基于亮度分区融合的高动态范围图像成像算法。首先,提取正常曝光彩色图像的亮度分量,根据亮度阈值将亮度分成两个区间;然后,对两个区间的图像用改进的指数函数扩展其亮度范围,使得低亮度区域的亮度增加、范围扩大,高亮度区域的亮度减小、范围扩大,从而增大图像的整体对比度,保留色彩和细节信息;最后,将扩展后的图像和原始正常曝光的图像基于模糊逻辑的方法融合为高动态图像。分别从主观和客观两方面对所提算法进行了分析。实验结果表明,所提算法能够有效地扩展图像的亮度范围,并保持场景的颜色信息和细节信息,生成的图像视觉效果更佳。     

High dynamic range image deghosting by fast approximate background modelling

High Dynamic Range (HDR) images of real world scenes often suffer from ghosting artifacts caused by motion in the scene. Existing solutions to this problem typically either only address specific types of ghosting, or are very computationally expensive.We address ghosting by performing change detection on exposure-normalized images, then reducing the contribution of moving objects to the final composite on a frame-by-frame basis. Change detection is computationally advantageous and it can be applied to images exhibiting varied ghosting artifacts. We demonstrate our method both for Low Dynamic Range (LDR) and HDR images. Additional constraints based on a priori knowledge of the changing exposures apply to HDR images. We increase the stability of our approach by using recent superpixel segmentation techniques to enhance the change detection. Our solution includes a novel approach for areas that see motion throughout the capture, e.g., foliage blowing in the wind.We demonstrate the success of our approach on challenging ghosting scenarios, and that our results are comparable to existing state-of- the-art methods, while providing computational savings over these methods.     

YUV空间的彩色图像HDR合成算法

RGB空间的高动态范围(HDR)彩色图像合成算法存在颜色失真和计算耗时的问题。为此,提出一种YUV空间的彩色图像HDR合成算法。利用多幅不同曝光度的普通数字图像,通过计算机高速运算对彩色图像进行HDR合成。实验结果表明,该算法的合成图像质量较高、计算速度较快。