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.     

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.     

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。     

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.     

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.     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

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.     

Tone-mapping high dynamic range images by novel histogram adjustment

In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images.     

全细节增强多曝光图像融合

目的 曝光融合算法,即将多幅不同曝光时间的图像融合得到一幅曝光度良好的图像,可能在最终的输出图像中引入光晕伪影、边缘模糊和细节丢失等问题。针对曝光融合过程中存在的上述问题,本文从细节增强原理出发提出了一种全细节增强的曝光融合算法。方法 在分析了光晕现象产生原因的基础上,从聚合的新角度对经典引导滤波进行改进,明显改善引导滤波器的保边特性,从而有效去除或减小光晕;用该改进引导滤波器提取不同曝光图像的细节信息,并依据曝光良好度将多幅细节图融合得到拍摄场景的全细节信息;将提取、融合得到的全细节信息整合到由经典曝光融合算法得到的初步融合图像上,最终输出一幅全细节增强后的融合图像。结果 实验选取17组多曝光高质量图像作为输入图像序列,本文算法相较于其他算法得到的融合图像边缘保持较好,融合自然;从客观指标看,本文算法在信息熵、互信息与平均梯度等指标上都较其他融合算法有所提升。以本文17组图像的平均结果来看,本文算法相较于经典的拉普拉斯金字塔融合算法在信息熵上提升了14.13%,在互信息熵上提升了0.03%,在平均梯度上提升了16.45%。结论 提出的全细节增强的曝光融合算法将加权聚合引导滤波用于计算多曝光序列图像的细节信息,并将该细节信息融合到经典曝光融合算法所得到的一幅中间图像之上,从而得到最终的融合图像。本文的处理方法使最终融合图像包含更多细节,降低或避免了光晕及梯度翻转等现象,且最终输出图像的视觉效果更加优秀。     

Uncertainty visualization using HDR volume rendering

In this paper, we explore a novel idea of using high dynamic range (HDR) technology for uncertainty visualization. We focus on scalar volumetric data sets where every data point is associated with scalar uncertainty. We design a transfer function that maps each data point to a color in HDR space. The luminance component of the color is exploited to capture uncertainty. We modify existing tone mapping techniques and suitably integrate them with volume ray casting to obtain a low dynamic range (LDR) image. The resulting image is displayed on a conventional 8-bits-per-channel display device. The usage of HDR mapping reveals fine details in uncertainty distribution and enables the users to interactively study the data in the context of corresponding uncertainty information. We demonstrate the utility of our method and evaluate the results using data sets from ocean modeling.     

An experimental evaluation of visual similarity for HDR images

In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control factors include choice of tone mapping operator (TMO), choice of distance metric, and choice of image feature. The image features that we experiment with are chosen from the features that are commonly used in the usual low dynamic range setting including features learned via Convolutional Neural Networks. The set of image features also includes combined features where the combination coefficients are estimated using logistic regression. We compute correlations between human judgments and quantitative features to understand how much each feature contributes to visual similarity. Combined features yield nearly 84% agreement with human judgments when applied on tone mapped images. Though we observed that using common features directly on raw or linearly scaled HDR images yield subpar correlation estimates compared to using them on tone mapped HDR images, we did not observe significant effect due to the choice of TMO on the estimates. As an application, we propose an improvement to style-based tone mapping for more correctly imparting desired styles to HDR images with different characteristics.

     

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.     

High-quality brightness enhancement functions for real-time reverse tone mapping

This paper presents an automatic technique for producing high-quality brightness-enhancement functions for real-time reverse tone mapping of images and videos. Our approach uses a bilateral filter to obtain smooth results while preserving sharp luminance discontinuities, and can be efficiently implemented on GPUs. We demonstrate the effectiveness of our approach by reverse tone mapping several images and videos. Experiments based on HDR visible difference predicator and on an image distortion metric indicate that the results produced by our method are less prone to visible artifacts than the ones obtained with the state-of-the-art technique for real-time automatic computation of brightness enhancement functions.     

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.     

Modeling a Generic Tone-mapping Operator

Although several new tone‐mapping operators are proposed each year, there is no reliable method to validate their performance or to tell how different they are from one another. In order to analyze and understand the behavior of tone‐mapping operators, we model their mechanisms by fitting a generic operator to an HDR image and its tone‐mapped LDR rendering. We demonstrate that the majority of both global and local tone‐mapping operators can be well approximated by computationally inexpensive image processing operations, such as a per‐pixel tone curve, a modulation transfer function and color saturation adjustment. The results produced by such a generic tone‐mapping algorithm are often visually indistinguishable from much more expensive algorithms, such as the bilateral filter. We show the usefulness of our generic tone‐mapper in backward‐compatible HDR image compression, the black‐box analysis of existing tone mapping algorithms and the synthesis of new algorithms that are combination of existing operators.     

一种改进的多尺度Retinex色调映射算法

传统的低动态范围显示设备不能很好地表现高动态范围图像信息,针对这一问题,提出一种基于引导滤波的Retinex多尺度分解色调映射算法。该算法使用引导滤波对光照信息进行估计,将高动态范围图像的亮度分为光照层和反射层;然后对反射层分量进行多尺度分解,得到一系列细节层和一个基本层,将细节层和基本层进行合并和色彩还原;最后得到色调映射后的图像。实验结果表明,该算法可以较好地还原真实场景信息,映射后图像的细节和对比度较好,色彩鲜艳。     

Image smoothing with generalized random walks: Algorithm and applications

A novel generalized random walks model based algorithm for image smoothing is presented. Unlike previous image smoothing methods, the proposed method performs image smoothing in a global weighted way based on graph notation, which can preserve important features and edges as much as possible. Based on the new random walks model, input image information and user defined smoothing scale information are projected to a graph, our method calculates the probability that a random walker starting at each pixel node position will first reach one of the pre-defined terminal node to achieve image smoothing, which goes to solving a system of linear equations, the system can be solved efficiently by lots of methods. Theoretical analysis and experimental results are reported to illustrate the usefulness and potential applicability of our algorithm on various computer vision fields, including image enhancement, edge detection, image decomposition, high dynamic range (HDR) image tone mapping and other applications.     

数字图像水印技术中滤波新方法

针对数字图像发生了旋转和其它变化时，一些滤波方法不能对图像中的数字水印进行识别的问题，基于对数极坐标映射（LPM），提出了一种数字图像水印滤波的新方法。为检测新设计的滤波方法的实用价值，进行了不同滤波器的数字图像水印识别能力的比较。结果表明：当含数字水印的图像发生小旋转和缩小变化，即：旋转10度和缩小为原图像的0．9倍时，纯相位滤波器（POF）不能对图像中的数字水印进行识别，但新设计的滤波器能对图像中数字水印具有较好的识别能力；当含数字水印的图像旋转45度，缩小为原图像的0．7倍以及旋转和缩小同时发生时，新设计的滤波器对图像变化后的数字水印仍有较好的识别能力。结果证明：这种新的滤波方法对数字图像水印的识别具有实用价值。