基于权重最小二乘结构边缘保持色调映射算法

高动态范围图像由于其动态范围超过普通显示器的动态范围,所以无法正常显示,从而需要研究在保留高动态范围图像对比度、细节信息以及色彩信息的情况下压缩高动态范围图像的动态范围以适应低动态范围显示器进行显示的色调映射算法。本文提出基于权重最小二乘结构的边缘保持图像平滑色调映射算法。首先,建立基于权重最小二乘的边缘保持图像平滑滤波算子;然后,将输入的高动态范围图像转换至NTSC空间分离亮度信息和颜色信息,利用该滤波算子对亮度信息进行多级分层,获得基本层以及多级细节层信息;最后,对基本层进行动态范围压缩,利用压缩后的基本层结合多级细节层信息并转换回RGB空间获得输出的低动态范围图像。文中通过实验采集的多曝光图像序列利用Debevec和Malik提出高动态范围图像融合算法获得拍摄场景的高动态范围图像,采用本文提出算法对高动态范围图像进行色调映射处理获得较为理想的保留图像有效信息的低动态范围图像,从而验证了文中提出算法的有效性。     

一种亮度分区和导向滤波相结合的色调映射算法

针对高动态范围(High Dynamic Range,HDR)图像的动态范围与普通显示设备不匹配的问题,本文提出了一种亮度分区和导向滤波相结合的色调映射算法。首先,通过颜色空间转换将输入的HDR图像转换到CIEL*a*b*空间,得到亮度层和色度层图像,接着对亮度层图像进行对数压缩并分区,对压缩后的各分区亮度图像选取合适γ值进行校正。然后,对于现有映射算法过于强调对图像亮度的处理而忽略色度的问题,本文将压缩后的亮度图像作为引导图像对色度层图像进行导向滤波处理。最后,将处理后的亮度层图像和色度层图像融合得到标准动态范围(Standard Dynamic Range,SDR)图像。通过对比实验表明,本文算法得到的图像在清晰度、信息熵、方差方面较全局映射法、局部映射法和双边滤波法平均提高了94.03%、30.73%和24.23%,说明本文算法在实现高动态图像亮度压缩的同时,更好地实现了对色度和局部细节的处理。     

海面红外图像的动态范围压缩及细节增强

动态范围压缩和细节增强是红外图像处理的两个重要课题。为了将高动态海面背景红外图像清晰显示,提出一种高动态范围压缩及细节增强算法。首先,通过基于梯度边缘信息的多方向拉普拉斯增强方法,将梯度图像平滑处理,并与多方向拉普拉斯滤波相乘,实现高动态范围图像的细节增强;然后统计增强后图像的动态广义直方图信息;最后采用灰度级分组的方法构造映射函数,将高动态范围压缩到8 bits,输出可清晰显示的红外图像。对大量海面背景红外图像进行实验分析,结果表明,该算法提高了图像的对比度,有效增强了舰船目标细节,同时抑制了海面背景噪声的放大和光晕现象的产生,最终获得较好的输出图像。     

色度亮度协同滤波的色调映射算法

针对高动态范围图像显现质量有待提高的问题,本文在色度亮度颜色空间中提出了一种基于协同滤波的色调映射算法.首先将输入的高动态范围图像利用色度亮度颜色空间来提取亮度信息和色度信息,然后分别将色度信息和亮度信息进行分解和重构.使用双边滤波技术对亮度信息分解得到亮度基本层和亮度细节层;根据亮度信息和色度信息大尺度边缘的一致性,...     

一种基于LCD-LED双调制显示器的HDR图像显示方法

提出一种针对HDR图像的显示方法,该方法应用于LCD-LED双调制高动态显示器,旨在使显示器更好地显示HDR图像要求的亮度细节,提高视觉质量。首先,建立HDR图像亮度与LED亮度范围对应关系,使用光扩散函数作为背光LED亮度取值范围的权重。然后,统计LED的亮度辐射区域内不同LED发光强度等级的权重,取权重最大者对应的亮度作为该背光LED亮度。最后,利用依据LED亮度生成LED背光扩散图像与HDR图像计算出LCD显示图像。仿真结果表明,该算法可以使显示器亮度失真率系数低至0.006以下。在LCD-LED双调制高动态显示器上实验验证结果表明,该算法可以有效提高图像的层次感,有效抑制LCD-LED双调制高动态显示器的"光晕"以及亮度截断现象,提高显示质量。     

一种新的红外图像细节增强算法

红外成像系统在数字化时使用14位(或16位)数据来量化一个像素,具有动态范围大、含噪多和对比度低等特点,因此在进一步处理之前往往需要进行必要的增强。目前常用的红外数据增强都是基于普通方法压缩至8位后完成,许多细节信息已在压缩过程中损失,因而增强效果有限。本文把增强过程放在从14位到8位的压缩过程中,提出一种双域滤波及改进的最值归一化对比度增强算法的红外图像增强方法。首先使用双域滤波将原始14位图像的细节部分与基本部分分开,压缩基本部分的动态范围、保留或增强细节部分,然后使用改进的最值归一化方法增强图像对比度,最终得到可以在普通显示器上处理的8位图像数据。该方法在压缩红外图像数据宽度的同时保留了细节信息,取得了良好的实验结果。     

基于多尺度的高动态红外图像增强算法

红外图像通常具有很高的动态范围,数字量化位数一般大于或等于14 bit,这样的高动态图像既包含大的信号变量也包含较小的低对比度细节,这对于人眼的理解和显示设备的渲染都是不能接受的。在多尺度和边缘保留型滤波器的框架下,提出了一种改进的红外图像增强算法。首先是应用加权最小二乘滤波器对原始图像进行边缘保留式的图像多尺度分层操作,然后分别对得到的多层细节图像和残留模糊图像进行处理,使细节得到适当的放大,而残留层得到压缩,最后再把细节层和残留层合并,得到最终用于显示的低动态范围图像。该方法可以较好的保留红外图像细节,同时避免或削弱由细节分离操作所带来的合成图像中的光晕和梯度反转现象。     

基于亮度自适应分段的高动态图像色调映射算法

高动态范围图像(High Dynamic Range Image,HDRI)反映了真实场景的亮度值范围,具有较大的动态范围,而传统的显示设备的动态范围较低.若将高动态范围图像直接压缩到低动态范围显示设备时,可能会导致图像失真、细节丢失等问题.为解决上述问题,本文提出一种基于亮度自适应分段式的色调映射算法.首先,将高动态范围图像从RGB彩色空间转化到CIE XYZ彩色空间,使得亮度信息和彩色信息分离,并只对亮度信息进行处理且不改变颜色信息.其次,以对数亮度平均值作为关键值,从而得到划分区间段的低光区阈值和高光区阈值.然后,针对不同阶段亮度值的特点采用不同的亮度压缩算法,获得新的亮度分量.最后,恢复颜色信息,得到低动态范围图像.实验结果表明,本文的色调映射算法得到的结果整体亮度和细节效果较好.     

基于改进引导滤波分层技术的红外图像增强算法

针对高动态范围红外图像在压缩显示过程中容易出现对比度低、细节模糊,以及传统增强算法在处理连续多帧的红外图像时亮度跳变的问题,提出了一种基于改进引导滤波分层技术的红外图像增强算法。该算法使用改进引导滤波对图像进行分层,从而得到质量更高的原始细节层,并使用噪声掩膜技术对其去噪;基础层使用改进的直方图均衡算法提升对比度;增强后的基础层与细节层加权融合,再通过基于神经网络的自适应伽马校正得到亮度恒定的增强图像。实验表明,相较对比算法,该算法在平均梯度上提升了2.8左右、在对比度增强测量指标上提升了10左右,同时使帧间亮度方差下降到了0.1,说明该算法具有较好的细节增强效果和亮度稳定性。     

基于超像素分割的红外图像细节增强算法

高动态范围的红外图像压缩和细节增强有利于提高人眼获取图像中关键细节信息的能力。因此,它是红外成像的重要研究课题之一。针对传统的全局色阶重建不能最优呈现红外图像细节层和基础层的问题,设计了对红外图像局部进行色阶重建的方案,并提出了一种基于超像素分割的红外图像动态范围压缩和细节增强方法。该方法首先采用超像素分割算法将原始红外图像分割成多个自相似子区域,然后对各个子区域进行压缩和细节增强。实验结果表明,该方法可以更有效地压缩和增强红外图像,在高动态范围压缩图像的同时能很好地保留原始图像的细节信息。     

On performance of lossless compression for HDR image quantized in color space

High dynamic range (HDR) image requires a higher number of bits per color channel than traditional images. This brings about problems to storage and transmission. Color space quantization has been extensively studied to achieve bit encodings for each pixel and still yields prohibitively large files. This paper explores the possibility of further compressing HDR images quantized in color space. The compression schemes presented in this paper extends existing lossless image compression standards to encode HDR images. They separate HDR images in their bit encoding formats into images in grayscale or RGB domain, which can be directly compressed by existing lossless compression standards such as JPEG, JPEG 2000 and JPEG-LS. The efficacy of the compression schemes is illustrated by presenting extensive results of encoding a series of synthetic and natural HDR images. Significant bit savings of up to 53% are observed when comparing with original HDR formats and HD Photo compressed version. This is beneficial to the storage and transmission of HDR images.     

Recovering high dynamic range by Multi-Exposure Retinex

The matter of generating high dynamic range (HDR) image from a number of differently exposed pictures arises to satisfy the needs of high-quality imaging and industrial applications. A number of HDR image generation algorithms have been proposed in the past. However, the HDR radiance map recovered by these classical methods cannot completely exclude the noisy pixels in the input images and thus are unable to produce the optimal result with highest possible SNR. In this paper we are going to introduce a new HDR generation algorithm based on the Multi-Exposure Retinex model deduced in this paper for HDR image composition. The luminance component L and the reflectance R are synthesized independently before being combined together. A novel R image composition method is introduced to help the composed result image reach the highest possible SNR. The method is tested on grey-level images in this paper, but it can be easily extended to the color-image version.     

基于S曲线全局动态调光的改进算法

S曲线全局动态调光算法可以降低LED液晶显示器的功耗,同时能够提高显示图像的静态对比度,但该算法会造成部分图像色彩失真和细节丢失。针对这一问题,本文提出一种图像细节层分离与视觉显著性理论相结合的S曲线改进算法。首先,将原始图像转换至HSV色彩空间进行亮度和色度分离;然后,在图像亮度分量上采用双边滤波得到图像的基础层与细节层,基础层采用S曲线进行动态范围拉伸,实现像素补偿,细节层则运用视觉显著性理论进行分区与权值增强,弥补由像素补偿带来的细节损失;最后,将处理后的各层图像转换至RGB空间显示。将本文算法的仿真结果与原S曲线算法的结果进行对比。结果显示,本文算法在维持原算法功耗降低和静态对比度提升水平不变的基础上,解决了原算法在部分图像中出现的色彩失真和细节丢失问题,提升了图像的视觉显示效果,同时本文算法的仿真结果具有更大的信息熵和平均梯度。     

Tone mapping with contrast preservation and lightness correction in high dynamic range imaging

In real-world environments, the human visual system perceives a wide range of luminance in a scene. However, the full range of tones in a high dynamic range (HDR) scene cannot be displayed on standard display devices due to their low dynamic range (LDR). Therefore, tone mapping is necessary to faithfully display a HDR scene on an LDR display device. Existing tone mapping methods have problems because details and contrast in a scene are not preserved faithfully, and they also distort the colors in a scene. Thus, we propose a tone mapping method for preserving the detail in an HDR scene using a weighted least squares filter, which preserves the global contrast in a scene by using a competitive learning neural network, before applying a tone reproduction operator to preserve the color without shifting the lightness. According to the Helmholtz–Kohlrausch effect, the perception of brightness depends on the lightness, chroma, and hue of a color. For example, the luminance of pixels with specific colors such as red and blue is low in an HDR scene. The proposed method corrects the lightness of pixels according to the color (i.e., lightness, chroma, and hue) of a tone-mapped image. Experimental results with several test sets of images demonstrated that the proposed tone mapping method with contrast preservation and lightness correction is more suitable for dynamic range compression than existing tone mapping methods, while it also preserves the color of a scene in an effective way.     

Lossless compression of HDR color filter array image for the digital camera pipeline

This paper introduces a lossless color filter array (CFA) image compression scheme capable of handling high dynamic range (HDR) representation. The proposed pipeline consists of a series of pre-processing operations followed by a JPEG XR encoding module. A deinterleaving step separates the CFA image to sub-images of a single color channel, and each sub-image is processed by a proposed weighted template matching prediction. The utilized JPEG XR codec allows the compression of HDR data at low computational cost. Extensive experimentation is performed using sample test HDR images to validate performance and the proposed pipeline outperforms existing lossless CFA compression solutions in terms of compression efficiency.     

Methods for improving the tone mapping for backward compatible high dynamic range image and video coding

Backward compatibility for high dynamic range image and video compression forms one of the essential requirements in the transition phase from low dynamic range (LDR) displays to high dynamic range (HDR) displays. In a recent work [1], the problems of tone mapping and HDR video coding are originally fused together in the same mathematical framework, and an optimized solution for tone mapping is achieved in terms of the mean square error (MSE) of the logarithm of luminance values. In this paper, we improve this pioneer study in three aspects by considering its three shortcomings. First, the proposed method [1] works over the logarithms of luminance values which are not uniform with respect to Human Visual System (HVS) sensitivity. We propose to use the perceptually uniform luminance values as an alternative for the optimization of tone mapping curve. Second, the proposed method [1] does not take the quality of the resulting tone mapped images into account during the formulation in contrary to the main goal of tone mapping research. We include the LDR image quality as a constraint to the optimization problem and develop a generic methodology to compromise the trade-off between HDR and LDR image qualities for coding. Third, the proposed method [1] simply applies a low-pass filter to the generated tone curves for video frames to avoid flickering during the adaptation of the method to the video. We instead include an HVS based flickering constraint to the optimization and derive a methodology to compromise the trade-off between the rate-distortion performance and flickering distortion. The superiority of the proposed methodologies is verified with experiments on HDR images and video sequences.