Colour histogram equalisation via least-squares fitting of isotropic Gaussian mixture to uniform distribution

An algorithm to extend greyscale histogram equalisation to colour images is proposed. Unlike most current techniques, the proposed method can generate a uniform colour histogram, thus minimising disparity between histogram and uniform distribution. The method is applicable to higher dimensions. The results show that the approach is valid.     

Filtering of multichannel SAR images

An explicit form of the linear multichannel synthetic aperture radar (SAR) intensity filter, which preserves radiometry while optimally reducing speckle is derived, together with a compact expression for the theoretical gain in equivalent numbers of looks (ENLs). The filter can be applied to mixed data types, which is demonstrated using a combination of ERS and JERS satellite data, and confirms the filter performance predicted by the theory. Tests indicate that a simplified form of the filter, which neglects correlation between images, gives an ENL only slightly less than optimal, while being much easier to implement. Exact analysis of the effect of estimating filter weights shows that the linear increase in ENL with the number of images predicted for the ideal filter does not occur. In practice, the ENL is affected by the window size used to estimate the weights and saturates as the number of images increases. An efficient recursive form of the filter is described, which is most naturally applied to multitemporal data for the practically important case where the current image is uncorrelated with previous images in a data sequence     

Least squares restoration of multichannel images

Multichannel restoration using both within- and between-channel deterministic information is considered. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using the set theoretic and the constrained optimization approaches. A geometric interpretation of the estimates of both filters is given. Color images (three-channel imagery with red, green, and blue components) are considered. Constraints that capture the within- and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Experiments using color images are described     

Show-through correction in scanned images using joint histogram

In scanning duplex printed documents, show-through effect is a common phenomenon where the text or image on one side of the duplex printed page is contaminated by that of the other side. In this paper, we propose an empirical model for show-through accounting for the dependency of back side absorptance weights on the back side reflectances to overcome the limitation of the space invariant point spread function as used in the previous model. The weight function of the proposed empirical model and other model parameters are calculated from a transfer function derived from the joint histogram of the reflectances of the two sides of the document. Show-through corrected images of the scanned documents are then obtained using the calculated parameters and the proposed empirical model. Simulation results are presented to demonstrate the effectiveness of the proposed method as compared to a popular method of show-through correction.     

Asymptotically optimal blind estimation of multichannel images.

Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the required second-order signal and noise statistics are not known in which the decorrelation is difficult or expensive to calculate. An asymptotically optimal estimation scheme proposed here uses a 2-D discrete wavelet transform to approximately decorrelate the signal in space and the discrete Fourier transform to decorrelate between channels. The coefficient weighting is replaced with a wavelet-domain thresholding operation to result in an efficient estimation scheme for both stationary and nonstationary signals. In contrast to optimal estimation, this new scheme does not require second-order signal statistics, making it well suited to many applications. In addition to providing vastly improved visual quality, the new estimator typically yields signal-to-noise ratio gains 12 dB or higher for hyperspectral imagery and functional magnetic resonance images.     

Restoration of color images by multichannel Kalman filtering

A Kalman filter for optimal restoration of multichannel images is presented. This filter is derived using a multichannel semicausal image model that includes between-channel degradation. Both stationary and nonstationary image models are developed. This filter is implemented in the Fourier domain and computation is reduced from O(Λ ³N³M⁴) to O(Λ³N³M² ) for an M×M N-channel image with degradation length Λ. Color (red, green, and blue (RGB)) images are used as examples of multichannel images, and restoration in the RGB and YIQ domains is investigated. Simulations are presented in which the effectiveness of this filter is tested for different types of degradation and different image model estimates     

Fractal capacity dimension of three-dimensional histogram from color images

To contribute to the important task of characterizing the complex multidimensional structure of natural images, a fractal characterization is proposed for the colorimetric organization of natural color images. This is realized from their three-dimensional RGB color histogram, by applying a box-counting procedure to assess the dimensionality of its support. Regular scaling emerges, almost linear over the whole range of accessible scales, and with non-integer slope in log-log allowing the definition of a capacity dimension for the histogram. This manifests a fractal colorimetric organization with a self-similar structure of the color palette typically composing natural images. Such a fractal characterization complements other previously known fractal properties of natural images, some reported recently in their colorimetric organization, and others reported more classically in their spatial organization. Such fractal multiscale features uncovered in natural images provide helpful clues relevant to image modeling, processing and visual perception.     

基于空间分布的红外图像直方图均衡算法

随着非制冷焦平面阵列(UFPA)红外探测器的日益成熟，红外热成像技术越来越广泛应用于公安、消防、军事、医学、监控等领域。红外图像直方图分布集中，对比度低并通常伴有大量散粒噪声，需要处理以改进视觉效果。文中提出一种基于空间分布的图像直方图均衡算法，在图像增强同时又适当抑制噪声提升，实验表明该算法优于HE、PE、HP等算法，并具有较快的计算速度。     

Generalized maximum a posteriori processing of multichannel images and applications

This paper considers several aspects of robust estimation in the restoration of mutichannel images. Robust functionals emerging from a generalized maximum a posteriori (MAP) approach are employed for the representation of both the noise and the signal statistics. Several linear multichannel techniques can be derived as special cases of the approach presented. In addition, the robust approach derives nonlinear algorithms that simultaneously account for the suppression of nominal noise and outliers, and for the efficient reconstruction of sharp detailed structure in the estimate. The robust multichannel approach is presented as a general approach for the regularization of the ill-posed restoration problem. From this perspective, we develop a method for the selection of the regularization parameter, which can be used in a wide variety of applications that may or may not involve noise outliers. We consider several issues associated with the application of robust algorithms to multichannel images, we discuss computational inefficiencies of such algorithms, and we propose approximations that are appropriate for their cost-efficient multichannel implementation. We demonstrate the robust approach in two examples from the rapidly developing fields of color image processing and multiresolution image processing in the wavelet domain.The research presented in this paper was partially supported by the Graduate School of the University of Minnesota under Summer Research Fellowship no. 15556 and Grant-in-Aid Award no. 15987.     

Optimal restoration of multichannel images based on constrained mean-square estimation

In this paper, we present an efficient algorithm for the problem of multichannel image restoration. Existing multichannel techniques do not provide sufficient flexibility for the simultaneous suppression of the noise process and the preservation of sharp detailed structure in the estimate. The approach introduced overcomes this inefficiency by introducing the prototype Wiener structure in the smoothing process of the estimate. The corresponding algorithm is obtained from the optimization of theconstrained mean-square-error (CMSE) criterion, which is interpreted as a structured regularized criterion. The CMSE estimate always has a meaningful structure and lies between the minimum mean-square error estimate and the pseudo-inverse solution. In addition, the CMSE approach enables the suppression of streak artifacts, which are often experienced due to the amplification of the noise process. In particular, the paper focuses on the selection of the regularization parameter, which can significantly affect the quality of the regularized estimate. Two selection techniques are introduced. The first technique develops the equivalence between the minimization of the weighted least-squares function and the solution of a set of nonlinear equations in the case of unstructured space-varying and nonstationary problem formulations. The second selection method is based on the Cramer-Rao bound on the expected variation of the stabilizing term. The CMSE approach is demonstrated through a multichannel restoration example and is compared to other restoration techniques.     

基于双直方图平移的彩色图像可逆数据隐藏

提出一种基于双直方图平移的彩色图像可逆数据隐藏算法.利用色彩分量间的相关性减小差值,使差值直方图更加紧凑、直方图的峰值更大.采用直接调整像素值的方法在差值中嵌人数据,由像素值的二次可调整性定位不可调整像素,并嵌入少量的标志信息代替定位图,从而提高嵌入容量.实验结果表明,本文算法在保证图像质量的同时大幅提高了嵌入容量,与...     

一种颜色直方图计算相似度的资产图片核对算法

固定资产清查是高校、企事业、科研院所等的一项例行性工作,资产清查过程中一般要求对固定资产拍照存档.资产清查收集到的资产图片保存至文件夹,其中有成千上万张资产图片,需要从中挑出来一部分资产图片丢弃,且需要被挑出来的资产有图有真相地存放在其它文件夹,同时2个文件夹中资产图片文件名、清晰度、大小等均不一,针对如何快速完成这个图片文件挑选过程,文中提出一种基于颜色直方图的资产图片相似度统计核对算法,并对其过程进行了验证,证明了算法的有效性.     

一种基于Otsu法和平台直方图均衡的红外图像增强算法

针对红外吊舱图像的特点,提出了一种基于Otsu法和平台直方图均衡的红外图像增强算法。首先分析图像的直方图,通过改进的Otsu法把图像分割为天空背景区和地面目标区,背景区以背景峰值与分割后背景区残留目标的局部峰值为依据,采用了一种自适应的方法来产生平台阈值,然后在背景区和目标区的灰度范围内进行直方图均衡化处理。该算法有效地抑制了背景区的过度增强,扩大了目标区的灰度范围,增强了细节部分。实验仿真结果验证了算法的有效性。     

Nonrigid registration of 3-d multichannel microscopy images of cell nuclei.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.     

基于中间均衡直方图的红外图像非均匀性校正

针对目前红外场景校正算法存在的鬼影和参数时间漂移,提出基于中间均衡直方图的单幅图像校正算法。算法将场景校正的思想运用到图像列上,通过计算每一列的中间直方图算法来替代原始列的直方图。实验结果表明,算法优于总变差算法,具有简单快速,易于工程应用的优点。     

基于改进直方图的红外图像增强方法

为了能在动态范围压缩的同时增强红外图像的对比度,提出了一种基于Sobel梯度直方图均衡算法（gradient histogram equalization,GHE）。与以往的直方图均衡化（histogram equalization,HE）方法不同,该方法自适应地为图像强梯度的灰阶分配高对比度,保留并增强16 bit图像中更多的细节。随后使用双Gamma映射对映射曲线进行调整,有效地抑制图像亮部的过曝现象,同时提高暗部的细节。该方法相比于传统的直方图均衡化算法在暗区细节处理、过曝抑制、对比度增强等方面都有较好的效果。     

Upscaling factor estimation on pre-JPEG compressed images based on difference histogram of spectral peaks

Image is one of the most widely used information carrier exchanged in the Internet, which raises a problem of privacy leakage. Private images are vulnerable to be intercepted and altered by an attacker, violating the owner’s privacy. When an image is tampered maliciously, it is often necessary to perform geometric transformations such as scaling to hide the traces of tampering, introducing resampling traces. In the last two decades, spectral analysis is the most commonly used method for resampling detection. However, since JPEG compression severely interferes the statistical characteristics of resampled images and introduces blocking artifacts, the robustness is really poor for most classical spectrum-based methods in the presence of JPEG compression. In this paper, we propose a method to estimate the upscaling factors of upscaled images in the presence of JPEG compression. A comprehensive analysis in spectrum of scaled images is given. We find that both the location and their difference of spectral peaks in the spectrum of the upscaled pre-JPEG images are related to the upscaling factor. Hence, we adopt the difference histogram of spectral peaks to screen candidate upscaling factors and obtain the final estimation by additional verification step according to the location of the spectral peaks. The experimental results demonstrate the effectiveness of the proposed method.     

Adaptive equalisation in oversampled subbands

The potential presence of fractional delays, nonminimum phase parts, and a colouring of the channel output can require adaptive equalisers to adapt very long filters, which can have slow convergence for LMS-type adaptive algorithms. The authors present a novel oversampled subband approach to adaptive equalisation, which can both significantly reduce computational complexity and improve convergence speed