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.     

色外观匹配的高动态范围图像再现

针对高动态范围(high dynamic range,HDR)图像再现因观察条件变化引起的再现图像与源图像色外观不一致问题,提出一种色外观匹配的HDR图像再现算法.首先将HDR图像的色度和亮度信息分离,通过估计源场景观察条件,利用色外观模型(color appearance model,CAM)将HDR图像再现至显示环境,保持源场景的色度外观;然后针对亮度图像,根据直方图特征进行自适应分区,构造分段线性色阶调整函数,将显示亮度范围动态分配给不同的亮度分区,以增强图像感知对比度,并利用双边滤波技术提取图像细节信息进行细节补偿;最后,将处理后的彩色和非彩色信息合成,并对亮度压缩带来的色外观变化进行色度校正,得到与源HDR图像色外观一致的低动态范围(low dynamic range,LDR)再现图像.实验表明,新算法在色外观保持、动态范围压缩和细节表现上均优于传统算法.     

单幅图像的高动态范围图像生成方法

为了将现有低动态范围(LDR)图像转换为高动态范围(HDR)图像,提出一种将单幅LDR图像转换成对应HDR图像的方法.该方法基于人类视觉系统(HVS)模型,首先分离出LDR图像的亮度分量和色度分量,对亮度分量构造其反色调映射函数,通过逐点映射得到亮度分量的反色调映射图像;然后对亮度分量进行光源采样处理,并采用高斯滤波和腐蚀操作模拟光线衰减效应,对光源采样图像进行高光区域的扩展;再构造分段函数对反色调映射图像和高光区域扩展图像进行融合,得到最终的亮度分量;最后合并该亮度分量和色度分量得到最终的HDR图像.实验结果表明,文中方法能通过单幅LDR图像得到HDR图像,处理效果较好,运行效率高,具有较好的鲁棒性.     

基于概率模型的高动态范围图像色调映射

提出了一种概率模型对HDR(high dynamic range)图像进行色调再生.分别对局部像素的色调能量分布与HDR/LDR(low dynamic range)间梯度变化约束建立概率统计模型,通过求解最大后验概率(maximum a posteriori,简称MAP)将整个色调映射过程转化为一个能量最小化问题.实验结果表明,所提出的基于概率模型的色调映射方法能够生成比以往方法具有更多视觉信息的LDR 图像,可用于高级图像编辑、显示设备开发等领域.     

亮度分区自适应对数色调映射算法*

色调映射可将高动态范围图像显示在低动态范围显示器上。常用的对数全局色调映射算法由于压缩范围有限容易引起细节丢失,为此本文给出一种基于亮度分区的自适应对数色调映射算法。首先将高动态范围图像由RGB颜色空间转换为XYZ颜色空间以提取图像亮度信息,然后将亮度图分为高、中、低三个照度区域。根据区域亮度属性实施对数色调映射实现动态范围局部压缩,并进行融合处理以消除区域交界处的显示效果。同时采用双边滤波技术进行细节补偿。实验结果表明,此算法能有效压缩动态范围并再现真实场景信息,同时可以保留丰富的细节。     

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。     

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。     

基于细节再现的高动态范围图像分层映射算法

针对当前映射算法中亮度的映射函数非适性而引起对比度过度压缩的问题,以及映射时亮度变化改变图像细节可见性的问题,提出了一种基于细节再现的高动态范围(HDR)图像分层映射算法。该算法采用视觉响应曲线作为基础层的映射函数,根据图像局部适应性亮度动态地映射亮度;同时,在Stevens效应的思想基础上依据映射前后亮度变化获得补偿系数,拉伸或压缩细节层。测试结果表明：该映射算法所得图像能正确再现更多的可见细节。     

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。     

不同曝光值图像的直接融合方法

提出了一种直接从同一场景多次不同曝光值下成像的LDR(low dynamic range)图像序列中提取每个像素位置最佳成像信息的图像融合方法,可以在无需任何拍摄相机参数及场景先验信息的情况下,快速合成适合在常规设备上显示的HDR(high dynamic range)图像.该方法利用特殊设计的鲁棒性曲线拟合算法建立LDR图像序列中每个像素位置像素值曲线的数学模型,并由此给出评价单个像素成像时曝光合适程度的标准和融合最佳成像像素信息的方法.对不同场景的大量实验结果显示,该方法的计算结果与传统HDR成像技术经过复杂的HDR重建和色调映射计算后得到的结果相当,但具有更高的计算效率,并同时对图像噪声、相机微小移动和运动目标的影响具有较好的鲁棒性.     

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.     

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.     

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.     

Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography

We propose a technique for fusing a bracketed exposure sequence into a high quality image, without converting to High dynamic range (HDR) first. Skipping the physically based HDR assembly step simplifies the acquisition pipeline. This avoids camera response curve calibration and is computationally efficient. It also allows for including flash images in the sequence. Our technique blends multiple exposures, guided by simple quality measures like saturation and contrast. This is done in a multiresolution fashion to account for the brightness variation in the sequence. The resulting image quality is comparable to existing tone mapping operators.     

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.     

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。     

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.