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.     

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.     

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.     

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.     

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.     

基于细节特征融合的低照度全景图像增强

为了提高低照度条件下采集的全景图像的视觉效果,提出一种基于细节特征加权融合的低照度全景图像增强算法.首先,利用双边滤波算法提取出图像的光照分量,并分别采用自适应伽马校正和对比度受限的自适应直方图均衡化算法对光照分量进行处理;然后,与原始光照信息进行加权融合得到校正后的光照分量,并在反射分量调整时,提出一种自适应调整函数来校正反射信息;最后,将光照分量与反射分量合并,以实现对低照度全景图像的增强.实验结果表明,所提出的算法在提高图像亮度的同时,可以增强图像细节信息,去除噪声,使增强后图像色彩信息更加丰富自然.     

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。     

顾及欠曝光亮度映射的HDR图像生成方法

针对重曝光过程中欠曝光像素点对周围像素点产生干扰,导致高动态范围(High dynamic range,HDR)图像产生伪影的现象,提出顾及欠曝光的亮度映射HDR图像生成方法。具体步骤为:(1)欠曝光分类。利用L2正则化逻辑非线性回归算法训练出欠曝光像素点的分类器。(2)对图像进行重曝光。非欠曝光区域使用线性拟合得到的亮度映射函数重新曝光,欠曝光区域利用长曝光区域进行补偿。(3)生成HDR图像。利用重曝光后图像的饱和度,对比度,曝光度和亮度信息生成权重图,再加权融合生成HDR图像。实验表明,该方法有效抑制合成图像的伪影,与已有方法相比,在清晰度、颜色协调性、边缘保持以及运行时间上都有较大提高。     

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。     

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。     

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.     

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.     

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.     

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.     

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.     

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.     

基于色调映射的快速低照度图像增强

在弱光条件下,图像通常具有低能见度。为了增强低照度图像,提出一种基于全局自适应色调映射的快速增强方法。将图像从RGB颜色空间变换到YUV颜色空间,对亮度通道进行双边滤波,得到基本层和细节层;对基本层图像进行自适应全局色调映射,再叠加细节层的图像信息;恢复图像色彩饱和度,并重新变换到RGB空间。该算法能快速实现增强,且增强效果更显著,尤其对有大面积低像素的图像处理得更好。     

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。