基于Retinex的JPEG图像增强新方法

针对现有JPEG图像压缩与增强的过程分离,且图像增强后容易产生块状效应的局限性,提出一种新的JPEG图像增强算法,该算法嵌入JPEG图像压缩框架之中,在量化步骤之前增强图像以充分利用图像原始信息。增强过程以Retinex理论为基础,将DCT系数分为照度分量(DC系数)和反射分量(AC系数),对DC系数进行动态范围调整,并利用DC系数的增强因子对AC系数做细节增强。最后,对调整后的DCT系数做平滑处理来抑制块效应。实验结果表明,与传统的其他算法相比,该算法能更好的增强细节信息和保持色彩信息,并能保持与标准JPEG同等的图像压缩比。     

离散余弦变换域内的Retinex图像增强模型研究

针对现有离散余弦变换( DCT)域图像增强算法大多统一处理DCT系数,且不能有效抑制块状效应的问题,将Retinex思想引入DCT域,提出一种新增强算法:将DCT系数分为入射分量和反射分量,构造一种带强光抑制的函数,将入射分量映射到理想的动态范围；定义一种频带限制的对比度,实现反射分量的局部细节增强；使用子块分解方法抑...     

DCT域水印嵌入新策略和算法

本文提出一种新的水印嵌入策略，将水印信号嵌入到原始图像块DCT域的AC分量和DC分量中。基于该策略提出了一个自适应图像水印算法。该算法先把原始图像和水印图像分别进行分块DCT，然后将水印信号的AC分量和DC分量以不同的能量分别嵌入到原始图像块DCT域的AC分量和DC分量中。实验结果表明，该算法具有良好的鲁棒性。     

低照度图像增强算法

针对多尺度Retinex算法(MSR)以及其它图像增强算法处理后的图像峰值信噪比较低的缺陷,本文提出结合Retinex理论,根据照射分量和反射分量的特性,对二者分别采用不同的非线性函数进行调整来提高图像的对比度和增强图像细节,并在低亮度区域进行噪声抑制的方法。实验结果表明,在HSV和RGB彩色空间,运用本算法处理在低照度情况下的图像,处理后图像的峰值性噪比以及对比度高于其他算法,并且处理速度也快于MSR算法。     

基于非下采样Shearlet变换与模糊对比度的合成孔径雷达图像增强

针对合成孔径雷达（SAR）图像在成像和传输过程中引入噪声和干扰从而导致图像清晰度下降、细节丢失等问题,提出了一种非下采样Shearlet变换（NSST）与模糊对比度的SAR图像增强算法。首先,原始图像经NSST分解成一个低频分量和若干个高频分量;然后对低频分量进行线性增强以提高整体对比度,对高频分量采用阈值法进行增强以去除图像中的噪声;接着对处理后的两部分分量进行NSST反变换得到重构图像;最后采用模糊对比度算法对重构图像进行增强,提高图像细节信息和层次感,得到增强后的图像。对40幅图像的实验结果表明,与直方图均衡化、多尺度Retinex增强算法、基于Shearlet变换和多尺度Retinex的遥感图像增强算法、基于剪切波域改进Gamma校正的医学图像增强算法相比,该算法的图像峰值信噪比至少提升了22.9%,均方根误差至少降低了36.2%,能明显提升图像的清晰度,使图像的纹理信息更加清晰。     

基于Retinex的自适应非均匀低照度图像增强算法

非均匀低照度图像会影响目标识别跟踪的效果,为了增强非均匀低照度图像,提出一种基于Retinex理论的自适应亮度层图像增强算法。首先将图像HSI模型中I层图像分离出来;然后通过Retinex理论和多尺度引导滤波器获得I层的入射分量,根据输入图像的均值自动获取调整参数k,对入射分量的亮度进行自适应调整;最后通过主成分分析法(PCA)提取两个图像中的细节特征并进行融合。对比实验表明,该算法能有效提升非均匀低照度图像的整体亮度和细节信息,同时减少非均匀照度的影响。     

基于DC和AC分量的二维数字水印算法

提出一种新的二维数字水印算法,该算法先把原始图像分块DCT,再对水印图像分块DCT,取每一图像块的直流系数和前12个DCT低频交流系数,分别构成水印信号的直流分量和交流分量。利用能量比确定各个子块的直流拉伸因子和交流拉伸因子。水印信号的直、交流分量以不同的能量分别嵌入到原始图像块DCT域的DC分量和AC分量中。实验结果表明,该算法具有良好的不可见性和稳健性。     

基于DC和AC分量的二维数字水印算法

提出一种新的二维数字水印算法,该算法先把原始图像分块DCT,再对水印图像分块DCT,取每一图像块的直流系数和前 12个DCT低频交流系数,分别构成水印信号的直流分量和交流分量.利用能量比确定各个子块的直流拉伸因子和交流拉伸因子.水印信号的直、交流分量以不同的能量分别嵌入到原始图像块DCT域的DC分量和AC分量中.实验结果表明,该算法具有良好的不可见性和稳健性.     

基于NLEMD的Retinex图像增强算法

为了将已有的受光照影响严重的图片恢复出细节信息更加丰富且光照平衡的图片,提出了基于NLEMD的Retinex图像增强算法.该算法采用多幅图像(其中包括一副待增强的图像和将待增强图像分别提高nl,n2,…,nm倍照射系数的多幅图像)协同NLEMD和Retinex算法来实现图像的增强.采用将待增强图像提高,l倍照射系数来获得多幅图像的目的是为了获得更丰富的图像暗处或明处的细节信息,然后利用NLEMD充分提取这些已知图像的各个频率信息;选取其中一幅图像经过NLEMD分解后所得的剩余分量,用Retinex增强算法增强;最后叠加所有图像的高频信息和Retinex算法增强后的剩余分量,得到增强后的图像.实验结果表明,算法增强后的图像克服了传统Retinex算法增强后图像对比度低和引入了噪声的缺陷,且暗处细节信息更加丰富,获得了更舒适的视觉效果.     

结合细节信息的自适应Retinex算法水下图像增强

针对Retinex算法应用于水下图像增强中,常出现颜色失真与图像细节增强相矛盾的现象,提出了结合细节信息的自适应多尺度Retinex水下图像增强算法。分析包含不同细节信息的水下图像对Retinex算法增强中卷积函数尺度大小的选择要求;采用图像梯度作为调节因子,自适应调整多尺度Retinex算子的权重,用于适应包含不同细节信息的水下图像对对比度增强的要求,有效地缓和了水下图像增强在颜色失真和细节对比度提升之间的矛盾。多组实验验证了该算法在去除水下图像的蓝绿背景、避免颜色失真、消除非均匀光照和图像细节增强等方面均优于传统多尺度和颜色保真的多尺度Retinex算法。     

Compressed image reproduction based on block decomposition

A compressed image reproduction scheme is proposed by properly decomposing and manipulating the coefficients of discrete cosine transform (DCT) directly in the compressed domain. The basic idea of the proposed approach is to decompose each DCT block into several sub-blocks and to adjust the brightness and detail components of a given image for compressing dynamic range and enhancing contrast. Image reproduction based on the subblock decomposition can be done more precisely than any approach based on the normal block-sized approach. First, DCT coefficients of each block are decomposed into several sub-blocks. Next each sub-block's coefficients are separated into brightness and detail components, and treated differently according to content analysis. Then, the enhanced coefficients are projected on the constraint sets to avoid some artefacts, and are composed back to the original order. The main advantages of the proposed algorithm are that (i) it can enhance the dynamic range and details without affecting the compressibility of the given image since it operates directly in the compressed domain, and (ii) it does not boost blocking artefacts around big edges without any further processing. In order to evaluate the proposed scheme, several base-line approaches are described and compared using enhancement quality measures.     

磁盘阵列错误隐藏的DCT系数重建算法研究

基于DCT变换的图象压缩，以块为单位进行DCT变换把图象从时域转换到领域，由于能量主要集中在少数低频系数，再结合量化、编码，可以实现有效的压缩，基于DCT的错误隐藏算法可用于客户端挖重建图象，隐藏存储系统或传输信道带来的错误，本文针对磁盘阵列中单个磁盘原失效，提出了三种客户端的基DCT编码的图象错误隐藏算法，并进行了验证。     

DCT域彩色图像增强技术研究

为了减少图像的存储量,提高图像的传输效率,大量的图像被压缩,因此基于压缩域图像的增强技术越来越引起人们的重视。介绍了DCT域彩色图像增强技术的各种算法,其问题是会在图像块的交界处产生块状效应。着重分析了CES算法产生块状效应的原因,提出了两种改进方法来抑制块状效应。仿真结果表明,改进的算法不仅能够很好地抑制块状效应,而且降低了计算的复杂度。     

基于统计特征的DCT压缩域纹理图像检索方法

提出了一种基于离散余弦变换（Discrete Cosine Transfrom,DCT）的纹理图像的检索方法.该方法在DCT压缩域,通过直接对DCT系数计算,获得图像纹理的统计特征,并作为检索的依据.理论分析和实验结果都表明,该方法具有很好的检索准确率和效率,并且对于旋转具有不变性.     

压缩域中基于自动标记的图像分割

针对传统像素域中图像分割算法计算复杂的缺陷,提出了一种压缩域中快速图像分割算法。对图像分块,提取离散余弦变换（DCT）系数结合颜色矩作为块特征,利用支持向量机（SVM）实现对压缩域中图像块的自动标记,采用提出的阈值最小生成树（TMST）算法对已标记块进行区域生长,应用形态学相关算法对分割出的图像进行修补。通过Corel图像数据库对提出的方法进行验证,结果表明该方法能够更加快速有效地进行图像分割。     

JPEG compressed image retrieval via statistical features

To improve efficiency of compressed image retrieval, we propose a novel statistical feature extraction algorithm in this paper to characterize the image content directly in its compressed domain. The statistical feature extracted is mainly through computing a set of moments directly from DCT coefficients without involving full decompression or inverse DCT. Following the algorithm design, a content-based image retrieval system is implemented especially targeting retrieving joint picture expert group compressed images. Theoretical analysis and experimental results support that the system is robust to translation, rotation and scale transform with minor disturbance, and the system achieves good performances in terms of retrieval efficiency and effectiveness.     

DCT域中任意比例的图像上下采样算法

在许多实际应用中, 为了满足传输信道和终端显示设备的要求, 需要通过上采样和下采样来改变图像的尺寸. 压缩域中的图像上下采样可以在空域中进行, 然而, 直接在压缩域中实现将更为快速. 本文根据空域中块与子块的相互关系以及酉变换对矩阵乘法的分配率, 提出了 DCT 域内任意比例图像上下采样算法. 与现存的算法相比, 本算法具有较高的信噪比, 较低的运算复杂度, 并适用于帧内与帧间编码的不同情况, 可应用于不同视频编码转码的实时处理.     

H.264中基于DCT域的视频可控加密研究

提出一种适用于H.264/AVC的DCT系数分层可控加扰算法,按照特定规律修改Intra帧的DCT系数,再利用帧间预测将加扰效果扩散至所有帧,最终实现视频的无级扰乱。实验结果表明,此算法克服以往空间加扰算法造成压缩效率下降的不足,具有很好的加扰效果,适用于H.264/AVC的视频系统中。     

Fuzzy intensification operator based contrast enhancement in the compressed domain

With the increasing sizes of high resolution images, their storage and processing directly in the compressed domain has significantly gained importance. Algorithms for compressed domain image processing provide a powerful computational alternative to classical (pixel level) based implementations. While linear algorithms can be applied straightforward to the JPEG compressed images, this is not the case for nonlinear image processing, as for example contrast enhancement algorithms. In this paper a new implementation in the compressed domain of a very efficient contrast enhancement, based on fuzzy set modeling and on a fuzzy intensification operator, is presented. The fuzzy set parameters are adaptively chosen by analyzing the statistics of the image data in the compressed domain, in order to optimally enhance the image contrast. The nonlinear enhancement procedure requires a grey level threshold, for which an adaptive implementation, taking into account the frequency content of each coefficient block in the DCT (Discrete Cosine Transform) encoded JPEG image is proposed. This guarantees the optimal quality at minimum computational cost. The experimental results for a set of various contrast images validate the good performance and functionality of the proposed implementation.     

No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images

In this paper, we propose a new no-reference image quality assessment for JPEG compressed images. In contrast to the most existing approaches, the proposed method considers the compression processes for assessing the blocking effects in the JPEG compressed images. These images have blocking artifacts in high compression ratio. The quantization of the discrete cosine transform (DCT) coefficients is the main issue in JPEG algorithm to trade-off between image quality and compression ratio. When the compression ratio increases, DCT coefficients will be further decreased via quantization. The coarse quantization causes blocking effect in the compressed image. We propose to use the DCT coefficient values to score image quality in terms of blocking artifacts. An image may have uniform and non-uniform blocks, which are respectively associated with the low and high frequency information. Once an image is compressed using JPEG, inherent non-uniform blocks may become uniform due to quantization, whilst inherent uniform blocks stay uniform. In the proposed method for assessing the quality of an image, firstly, inherent non-uniform blocks are distinguished from inherent uniform blocks by using the sharpness map. If the DCT coefficients of the inherent non-uniform blocks are not significant, it indicates that the original block was quantized. Hence, the DCT coefficients of the inherent non-uniform blocks are used to assess the image quality. Experimental results on various image databases represent that the proposed blockiness metric is well correlated with the subjective metric and outperforms the existing metrics.